Chapter 5: Beyond Text: Multimodal LLMs
5.3 Video and Cross-Modal Research Directions
So far, we've seen how multimodal models can read text, interpret images, and even listen to audio. But the real world unfolds in time. Understanding video means not just recognizing objects in frames, but reasoning about motion, causality, and events across time. This temporal dimension adds tremendous complexity because it requires models to track entities as they move, change, appear, and disappear throughout a sequence.
For instance, understanding a cooking video requires tracking ingredients as they transform through various stages of preparation. Similarly, comprehending sports footage demands following players as they move across a field and interact with each other and the ball. These capabilities go far beyond static image recognition, requiring the model to maintain a coherent understanding of objects' identities and relationships as they evolve through time.
At the same time, many real-world tasks require more than one sense at once. A teacher may speak while showing slides, or a person may gesture while giving instructions. This is where cross-modal reasoning becomes essential — connecting multiple streams of input into a unified interpretation. Humans naturally integrate information across senses, allowing us to connect a speaker's voice to their facial movements, associate sounds with their visual sources, and understand demonstrations that combine verbal explanations with visual examples.
Creating AI systems with this capability requires sophisticated architectures that can not only process each modality effectively but also align and integrate information across them. For example, when watching an instructional video, the system must synchronize the spoken narration with the corresponding visual demonstrations, understanding which words refer to which objects or actions on screen. This form of cross-modal grounding is fundamental to creating truly helpful assistants that can understand and engage with the world as humans do.
5.3.1 Video Understanding with Transformers
Unlike images, video is a sequence of frames — and sequences are exactly what transformers excel at. The challenge is that video sequences are often huge: a 10-second clip at 30 FPS has 300 frames. Feeding all of that directly into a transformer would be computationally impossible with current hardware limitations.
For context, many image-based transformers struggle with processing even a few high-resolution images simultaneously, so handling hundreds of sequential frames would require exponentially more memory and processing power. The computational complexity grows quadratically with sequence length due to the self-attention mechanism, where each token must attend to every other token. With video, this problem is magnified dramatically.
To illustrate the scale of this challenge: if a single high-resolution frame requires processing 1,024 tokens (a modest estimate), then a 10-second video at 30 FPS would need to process 307,200 tokens simultaneously. The self-attention computation for this would involve approximately 94 billion token-to-token comparisons. Even with modern GPUs and TPUs, this is prohibitively expensive in terms of both memory requirements and computational time.
This computational burden becomes even more significant when we consider real-world applications. For instance, video analysis for security surveillance might involve processing hours of footage, potentially generating millions of frames. Similarly, content moderation systems for social media platforms need to analyze thousands of videos uploaded every minute. The sheer volume of data makes naive approaches to video processing with transformers unfeasible.
The memory requirements also present a substantial barrier. Self-attention matrices grow quadratically with input length, so a video with twice as many frames requires four times the memory. Modern GPUs typically have 16-80GB of VRAM, which would be quickly exhausted by even modest-length videos processed at full resolution. This memory constraint has forced researchers to develop specialized architectures and optimization techniques specifically for video understanding.
Additionally, video data presents unique temporal dependencies that span across frames. While a transformer could theoretically capture these relationships, the sheer volume of cross-frame connections creates a computational bottleneck that requires innovative architectural solutions beyond simply scaling up existing image transformer models.
Techniques to Handle Video
Frame sampling
Select only key frames or use a sliding window. This approach reduces computational load by choosing representative frames at regular intervals (e.g., every 5th frame) or focusing on frames with significant visual changes. While this sacrifices some temporal detail, it captures the essential content while making processing feasible.
Frame sampling is particularly effective when videos contain redundant information across consecutive frames. For example, in a surveillance video where the scene remains mostly static, processing every frame would be wasteful. By intelligently selecting frames, models can maintain high accuracy while dramatically reducing processing requirements.
The selection process can employ various strategies beyond simple fixed-interval sampling. Adaptive sampling techniques can analyze motion vectors or pixel differences between frames to determine when important changes occur. This allows more frames to be sampled during high-action sequences and fewer during static scenes, optimizing the information-to-computation ratio.
Additionally, sliding window approaches maintain temporal continuity by processing overlapping sets of frames. Rather than treating each frame in isolation, these methods analyze short sequences (e.g., 8-16 frames) at a time, sliding the window forward to progress through the video. This preserves short-term temporal relationships while keeping computation manageable.
In more detail, frame sampling works by strategically selecting a subset of frames from the complete video sequence. There are several methods for this selection, each with its own advantages for different video analysis scenarios:
- Uniform sampling: Taking frames at fixed intervals (e.g., one frame per second) to provide an even representation across the entire video. This approach is computationally efficient and works well for videos with consistent action or gradual changes. Uniform sampling reduces the computational burden by processing only a fraction of the total frames while maintaining temporal coverage across the entire video duration.When implementing uniform sampling, researchers typically define a sampling rate based on factors like video length, content type, and available computational resources.
For instance, action-heavy videos might require higher sampling rates (e.g., 2-3 frames per second) to capture quick movements, while slow-changing scenes might need only one frame every few seconds.The main advantage of uniform sampling is its simplicity and predictability. Since frames are selected at regular intervals, the model receives a consistent temporal distribution that spans the entire video without bias toward any particular segment. This helps prevent overfitting to specific temporal regions and ensures the model learns patterns that generalize across the entire timeline.
For example, in a wildlife documentary tracking animal migration, capturing one frame every few seconds can adequately represent the overall movement patterns while significantly reducing processing requirements. This approach would effectively showcase the gradual progression of herds across landscapes without needing to process every minute detail of movement between consecutive frames. The sampling rate can be adjusted based on the speed of migration – faster movements might require more frequent sampling, while slower journeys could be represented with fewer frames.
- Content-aware sampling: Using algorithms to detect significant visual changes and selecting frames only when meaningful transitions occur. This is particularly useful for videos with static scenes interrupted by important events.These methods analyze frame-to-frame differences in features like color histograms, edge patterns, or motion vectors to identify when something interesting happens. In surveillance footage, for instance, this approach might capture frames only when a person enters the frame, ignoring long periods where nothing changes.Content-aware sampling works by establishing baseline metrics for the visual content, then continuously monitoring for deviations that exceed predefined thresholds.
For example, the system might calculate the pixel-wise difference between consecutive frames, the change in distribution of colors, or the emergence of new edge patterns that could indicate new objects.More sophisticated implementations use computer vision techniques such as object detection and tracking to identify semantically meaningful changes. Rather than just measuring raw pixel differences, these systems can recognize when a new person appears, when an object moves significantly, or when the overall scene composition changes.
The computational efficiency gained through content-aware sampling can be dramatic. In a typical 24-hour surveillance video where activity occurs for only 30 minutes total, this approach might reduce the processing load by 97%, while still capturing all relevant events. This makes real-time video analysis feasible even with limited computing resources.Beyond surveillance, content-aware sampling proves valuable in domains like autonomous driving (capturing frames when traffic conditions change), medical monitoring (detecting significant patient movements), and sports analytics (identifying key plays in lengthy game footage).
- Keyframe extraction: Identifying frames that contain the most representative or information-rich content, often based on visual features or scene boundaries. These algorithms use techniques like clustering, where frames are grouped based on visual similarity, and the most central frame from each cluster is selected. This approach effectively condenses videos into their essential visual components while discarding redundant or transitional frames.
The clustering process typically involves converting each frame into feature vectors using techniques like convolutional neural networks (CNNs), then applying algorithms such as k-means or hierarchical clustering to group similar frames. Once clusters are formed, the frame closest to each cluster's centroid is selected as the keyframe, providing a diverse yet comprehensive sampling of the video's visual content.
For example, in a 30-minute documentary, keyframe extraction might identify just 20-30 frames that collectively represent all the major scenes, locations, and subjects, drastically reducing the processing requirements while preserving the core visual narrative.
Advanced methods may incorporate semantic understanding to identify frames that best capture the narrative elements of a video, such as those showing critical actions in a sports highlight or key emotional moments in a movie scene. These approaches go beyond low-level visual features to consider higher-level concepts like object interactions, facial expressions, and scene composition.
Modern keyframe extraction systems often employ deep learning models trained to recognize important visual moments based on millions of human-annotated videos. This allows them to prioritize frames with storytelling significance rather than just visual distinctiveness. For instance, in an interview video, the system might select frames showing important gestures or facial reactions rather than visually different but narratively insignificant background changes.
Some systems also incorporate additional contextual cues like audio peaks, subtitle changes, or scene transitions to better identify moments of importance. This multimodal approach ensures that keyframes align with significant developments in the video's content rather than just visual variations.
The computational benefits are substantial. For example, processing just 10% of frames can reduce memory requirements by 90% and computation time by a similar amount. This makes previously impossible tasks manageable with current hardware.
However, there are tradeoffs to consider. Fast-moving objects might appear to "teleport" between sampled frames, and subtle movements might be missed entirely. Researchers mitigate these issues by combining frame sampling with optical flow estimation or interpolation techniques that can reconstruct information about the skipped frames.
Temporal embeddings
Add position encodings for time as well as space. This technique extends the transformer's position embeddings to include temporal information, allowing the model to understand both where objects are located within frames and how they move across frames. These encodings help the model distinguish between identical frames appearing at different points in a sequence.
Temporal embeddings are crucial for video understanding because they provide essential context about when events occur in a sequence. Just as spatial position embeddings help transformers understand the arrangement of elements within an image, temporal embeddings encode the chronological order and relative timing of frames. This temporal awareness is particularly important when analyzing activities that unfold over time, like a person throwing a ball or a car turning at an intersection.
Without such temporal context, a model would struggle to differentiate between similar-looking frames that appear at different times in a video. For instance, in a cooking video where ingredients are added to a pot multiple times, the model needs to understand the sequence of additions to correctly interpret the recipe steps. Temporal embeddings provide this critical ordering information.
These embeddings can be implemented in several ways. One approach uses sinusoidal functions similar to those in the original transformer architecture, but with an additional dimension for time. Another method employs learnable embeddings specifically trained to capture temporal relationships. Some advanced systems use a combination of absolute time position (the frame's position in the entire sequence) and relative timing information (how far apart frames are from each other).
The sinusoidal approach has the advantage of being able to generalize to sequence lengths not seen during training, while learnable embeddings often capture more nuanced temporal patterns but may struggle with very long sequences. Researchers often experiment with both approaches to find the optimal solution for specific video understanding tasks.
Some advanced implementations also incorporate temporal embeddings at multiple scales. For instance, they might encode information about a frame's position within a second, a minute, and the entire video. This multi-scale approach helps models understand both fine-grained actions and longer narrative arcs within videos.
For example, in a video of a basketball game, temporal embeddings would help the model recognize that a player jumping, then releasing a ball, followed by the ball moving through a hoop represents a shooting sequence. Without temporal embeddings, these frames might be interpreted as disconnected events rather than a coherent action. The embeddings provide the critical temporal context that links these frames into a meaningful sequence.
Similarly, in a surveillance video, temporal embeddings allow the model to track individuals across frames and understand the progression of activities. This capability is essential for applications like activity recognition, where the order of actions defines the activity (e.g., entering a building versus leaving it involves the same frames in reverse order).
Hierarchical modeling
First process frames locally, then reason globally across segments. This multi-level approach initially treats smaller chunks of consecutive frames as units for local processing, extracting features about motion and changes. This hierarchical structure mirrors how humans understand videos - we first comprehend small actions and then connect them into larger narratives. The hierarchical approach is inspired by cognitive science research showing that human perception operates at multiple temporal scales simultaneously, from millisecond reactions to minute-long comprehension of complex scenes.
At the local level, models typically process 8-16 consecutive frames using lightweight attention mechanisms. This allows the model to capture short-term dynamics like object movement, facial expressions, or scene transitions without requiring extensive computational resources. These local processors extract rich representations that summarize what's happening in each small segment of video. The temporal receptive field at this level is carefully balanced - too few frames would miss important motion patterns, while too many would increase computational burden exponentially. Research has shown that 8-16 frames typically provides sufficient context to identify atomic actions while remaining computationally feasible.
These local processors employ specialized architectures like factorized attention or 3D convolutions that efficiently model spatiotemporal relationships. Some implementations use causal masking to ensure the model only attends to current and past frames, enabling real-time processing for applications like autonomous driving or security monitoring. Others process bidirectionally to maximize information extraction for offline analysis.
Then, a higher-level transformer processes these compressed representations to understand longer-term patterns and relationships across the entire video, effectively compressing the temporal dimension while preserving critical information. This global processor receives the local features as input and applies attention across them, enabling the model to recognize complex patterns like cause-effect relationships, recurring motifs, or narrative arcs that span minutes rather than seconds. The global transformer's design often includes specialized mechanisms for handling temporal distance, such as relative position encodings that help the model understand how far apart events occur in time.
This multi-resolution approach also addresses the challenge of variable information density in videos. Action-packed segments might require more detailed analysis, while static scenes need less processing. Advanced implementations dynamically allocate computational resources based on content complexity, spending more computation on informative segments.
For example, in a cooking video, local processing might identify individual actions like "chopping vegetables" or "stirring pot," while global processing would connect these into the complete recipe sequence and understand the relationship between early preparation steps and the final dish. This two-tier approach dramatically reduces computational complexity compared to processing all frames simultaneously while maintaining the ability to capture both fine-grained motions and long-range dependencies. In practical terms, this hierarchical design can reduce memory requirements by 80-90% compared to flat attention across all frames, making it possible to analyze longer videos on standard hardware.
5.3.2 VideoGPT
VideoGPT is a generative model for video built on the transformer architecture, advancing beyond static image generation by incorporating temporal aspects. This model adapts the powerful capabilities of transformer-based language models to the video domain, enabling it to understand and generate complex visual sequences over time. By extending the core principles of text generation to video, VideoGPT demonstrates how the transformer paradigm can be effectively applied across different modalities.
VideoGPT treats video as a sequence of image tokens, converting each frame into a discrete representation that can be processed sequentially. This tokenization process typically involves using a VQ-VAE (Vector Quantized Variational Autoencoder) to compress video frames into a more manageable representation. The resulting tokens form a vocabulary of visual elements that the model can manipulate, similar to how language models work with word tokens. This compression step is crucial because it reduces the dimensionality of the raw video data from millions of pixel values to thousands of discrete tokens, making the subsequent modeling task computationally feasible while preserving essential visual information.
The tokenization process works by training the VQ-VAE to encode frames into a latent space and then quantizing these continuous representations into a finite set of discrete codes from a learned codebook. This codebook effectively becomes the "visual vocabulary" of the model. The transformer component of VideoGPT then learns to predict the next token in this sequence, capturing both short-term frame-to-frame transitions and longer-term visual narratives.
The model learns both spatial and temporal dynamics, enabling frame prediction and video synthesis. This dual capability allows VideoGPT to understand not just what objects appear in a scene (spatial understanding), but how they move and interact over time (temporal understanding). The spatial component handles the composition within individual frames—elements like object appearance, lighting, and scene layout. The temporal component captures motion patterns, object persistence, and causal relationships between events across frames.
This architecture employs masked modeling techniques similar to those used in language models, where certain tokens are hidden during training and the model must predict them. In VideoGPT's case, this might involve masking future frames and training the model to predict them based on past frames, essentially learning to forecast how scenes will evolve.
For example, it can predict how a bouncing ball will continue its trajectory or how water will flow in a stream. This makes VideoGPT useful for applications like video completion, prediction of future frames, and even fully generative video synthesis from text prompts. Beyond these creative applications, the model's understanding of visual dynamics can benefit fields like robotics (predicting physical interactions), autonomous driving (anticipating traffic movements), and virtual reality (generating realistic environmental responses).
Example: Implementing VideoGPT: Video Generation with Transformers
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import numpy as np
import os
from tqdm import tqdm
# 1. VQ-VAE for Video Frame Tokenization
class VQVAE(nn.Module):
def __init__(self, input_dim=3, hidden_dim=128, num_embeddings=1024, embedding_dim=64):
super().__init__()
# Encoder: Convert raw frames to continuous latent space
self.encoder = nn.Sequential(
nn.Conv2d(input_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_dim, embedding_dim, kernel_size=3, stride=1, padding=1)
)
# Vector Quantization: Map continuous vectors to discrete codebook entries
self.codebook = nn.Embedding(num_embeddings, embedding_dim)
self.codebook.weight.data.uniform_(-1.0 / num_embeddings, 1.0 / num_embeddings)
# Decoder: Reconstruct frames from quantized tokens
self.decoder = nn.Sequential(
nn.Conv2d(embedding_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(hidden_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(hidden_dim, input_dim, kernel_size=4, stride=2, padding=1),
nn.Tanh()
)
def encode(self, x):
z = self.encoder(x)
return z
def quantize(self, z):
# Reshape z for quantization
z_flattened = z.permute(0, 2, 3, 1).contiguous().view(-1, z.shape[1])
# Calculate distances to codebook vectors
d = torch.sum(z_flattened**2, dim=1, keepdim=True) + \
torch.sum(self.codebook.weight**2, dim=1) - \
2 * torch.matmul(z_flattened, self.codebook.weight.t())
# Find nearest codebook vector
min_encoding_indices = torch.argmin(d, dim=1)
z_q = self.codebook(min_encoding_indices).view(z.shape[0], z.shape[2], z.shape[3], z.shape[1])
z_q = z_q.permute(0, 3, 1, 2).contiguous()
# Straight-through estimator for gradients
z_q_sg = z + (z_q - z).detach()
return z_q_sg, min_encoding_indices.view(z.shape[0], z.shape[2], z.shape[3])
def decode(self, z_q):
return self.decoder(z_q)
def forward(self, x):
z = self.encode(x)
z_q_sg, indices = self.quantize(z)
x_recon = self.decode(z_q_sg)
return x_recon, z, z_q_sg, indices
# 2. Transformer for Video Prediction
class VideoGPTTransformer(nn.Module):
def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6,
dim_feedforward=2048, max_seq_length=256):
super().__init__()
self.d_model = d_model
# Token embedding: Convert discrete tokens to continuous vectors
self.token_embedding = nn.Embedding(vocab_size, d_model)
# Position encoding: Add information about token position in sequence
self.pos_encoder = nn.Parameter(torch.zeros(1, max_seq_length, d_model))
# Transformer encoder layers
encoder_layers = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True
)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
# Output head: Project to token probabilities
self.output_head = nn.Linear(d_model, vocab_size)
def forward(self, src, src_mask=None):
# src shape: [batch_size, seq_len]
batch_size, seq_len = src.shape
# Embed tokens and add positional encoding
src = self.token_embedding(src) * np.sqrt(self.d_model)
src = src + self.pos_encoder[:, :seq_len, :]
# Pass through transformer
output = self.transformer_encoder(src, src_mask)
# Project to vocabulary space
output = self.output_head(output)
return output
# 3. Dataset for processing video frames
class VideoDataset(Dataset):
def __init__(self, video_dir, frame_size=(64, 64), frames_per_clip=16, transform=None):
self.video_paths = [os.path.join(video_dir, f) for f in os.listdir(video_dir)
if f.endswith(('.mp4', '.avi'))]
self.frame_size = frame_size
self.frames_per_clip = frames_per_clip
self.transform = transform or transforms.Compose([
transforms.Resize(frame_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
def __len__(self):
return len(self.video_paths)
def __getitem__(self, idx):
import cv2
video_path = self.video_paths[idx]
cap = cv2.VideoCapture(video_path)
# Calculate frame sampling
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_indices = np.linspace(0, total_frames-1, self.frames_per_clip, dtype=int)
# Extract frames
frames = []
for frame_idx in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
ret, frame = cap.read()
if ret:
# Convert BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Apply transforms
if self.transform:
frame = self.transform(frame)
frames.append(frame)
cap.release()
# Stack frames along a new dimension
return torch.stack(frames) # Shape: [frames_per_clip, channels, height, width]
# 4. Training Functions
def train_vqvae(vqvae, dataloader, optimizer, epochs=10, device='cuda'):
vqvae.to(device)
for epoch in range(epochs):
total_loss = 0
for batch_idx, frames in enumerate(tqdm(dataloader)):
frames = frames.to(device) # [B, T, C, H, W]
batch_size, time_steps = frames.shape[:2]
# Reshape to process all frames at once
frames_flat = frames.view(-1, *frames.shape[2:]) # [B*T, C, H, W]
optimizer.zero_grad()
# Forward pass through VQ-VAE
x_recon, z, z_q, indices = vqvae(frames_flat)
# Calculate losses
recon_loss = F.mse_loss(x_recon, frames_flat)
# VQ-VAE commitment loss
vq_loss = F.mse_loss(z_q, z.detach())
commitment_loss = F.mse_loss(z, z_q.detach())
# Combined loss
loss = recon_loss + vq_loss + 0.25 * commitment_loss
# Backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
return vqvae
def train_transformer(transformer, vqvae, dataloader, optimizer, epochs=10, device='cuda'):
transformer.to(device)
vqvae.to(device).eval()
for epoch in range(epochs):
total_loss = 0
for batch_idx, frames in enumerate(tqdm(dataloader)):
frames = frames.to(device) # [B, T, C, H, W]
batch_size, time_steps = frames.shape[:2]
# Reshape to process all frames at once
frames_flat = frames.view(-1, *frames.shape[2:]) # [B*T, C, H, W]
# Get token indices from VQ-VAE
with torch.no_grad():
z = vqvae.encode(frames_flat)
_, indices = vqvae.quantize(z)
# Reshape indices back to [batch_size, time_steps, height, width]
indices = indices.view(batch_size, time_steps, *indices.shape[1:])
# Flatten spatial dimensions to get sequence of tokens per frame
# [batch_size, time_steps, height*width]
token_sequences = indices.reshape(batch_size, time_steps, -1)
# For transformer training, we predict next tokens
src = token_sequences[:, :-1].reshape(batch_size, -1) # Input sequence
tgt = token_sequences[:, 1:].reshape(batch_size, -1) # Target sequence
optimizer.zero_grad()
# Create attention mask (optional for training efficiency)
seq_len = src.shape[1]
attn_mask = torch.triu(torch.ones(seq_len, seq_len) * float('-inf'), diagonal=1).to(device)
# Forward pass
output = transformer(src, attn_mask)
# Calculate loss
loss = F.cross_entropy(output.reshape(-1, output.size(-1)), tgt.reshape(-1))
# Backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
return transformer
# 5. Main: Putting it all together
def main():
# Hyperparameters
batch_size = 8
frames_per_clip = 16
frame_size = (64, 64)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Create dataset and dataloader
dataset = VideoDataset(
video_dir="path/to/videos",
frame_size=frame_size,
frames_per_clip=frames_per_clip
)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)
# Step 1: Train VQ-VAE
vqvae = VQVAE(input_dim=3, hidden_dim=128, num_embeddings=1024, embedding_dim=64)
vqvae_optimizer = torch.optim.Adam(vqvae.parameters(), lr=3e-4)
vqvae = train_vqvae(vqvae, dataloader, vqvae_optimizer, epochs=10, device=device)
# Save VQ-VAE model
torch.save(vqvae.state_dict(), "vqvae_model.pth")
# Step 2: Train Transformer
# Number of tokens = codebook size (from VQ-VAE)
vocab_size = 1024 + 1 # +1 for padding token
transformer = VideoGPTTransformer(vocab_size=vocab_size, d_model=512, nhead=8, num_layers=6)
transformer_optimizer = torch.optim.Adam(transformer.parameters(), lr=1e-4)
transformer = train_transformer(transformer, vqvae, dataloader, transformer_optimizer, epochs=20, device=device)
# Save Transformer model
torch.save(transformer.state_dict(), "transformer_model.pth")
# 6. Video Generation Function
def generate_video(vqvae, transformer, seed_frames, num_frames_to_generate=16, device='cuda'):
vqvae.to(device).eval()
transformer.to(device).eval()
# Process seed frames through VQ-VAE to get tokens
with torch.no_grad():
seed_frames = seed_frames.to(device)
z = vqvae.encode(seed_frames)
_, indices = vqvae.quantize(z)
# Flatten spatial dimensions to get sequence of tokens
token_sequence = indices.reshape(1, -1) # [1, time*height*width]
# Generate new frames one by one
generated_tokens = token_sequence.clone()
for _ in range(num_frames_to_generate):
# Predict next tokens
with torch.no_grad():
output = transformer(generated_tokens)
next_token_logits = output[:, -1, :]
next_tokens = torch.argmax(next_token_logits, dim=-1, keepdim=True)
generated_tokens = torch.cat([generated_tokens, next_tokens], dim=1)
# Extract only the newly generated tokens
new_tokens = generated_tokens[:, token_sequence.shape[1]:]
# Reshape tokens to match expected input for VQ-VAE decoder
h, w = indices.shape[1], indices.shape[2]
new_tokens = new_tokens.reshape(-1, h, w) # [num_frames_to_generate, height, width]
# Decode tokens to frames
generated_frames = []
with torch.no_grad():
for tokens in new_tokens:
tokens = tokens.unsqueeze(0) # Add batch dimension
z_q = vqvae.codebook(tokens.view(-1)).view(1, -1, vqvae.codebook.embedding_dim)
z_q = z_q.permute(0, 2, 1).view(1, vqvae.codebook.embedding_dim, h, w)
frame = vqvae.decode(z_q)
generated_frames.append(frame)
# Stack frames along time dimension
return torch.cat(generated_frames, dim=0) # [num_frames_to_generate, C, H, W]
if __name__ == "__main__":
main()
Detailed Explanation of VideoGPT Implementation
This example demonstrates a comprehensive approach to video generation using the VideoGPT architecture. Let's break down the key components:
1. Vector Quantized Variational Autoencoder (VQ-VAE)
The VQ-VAE forms the foundation of VideoGPT by converting raw video frames into discrete tokens:
- Encoder: Compresses video frames into a lower-dimensional continuous latent space using convolutional layers.
- Vector Quantization: Maps these continuous vectors to the nearest vectors in a learned "codebook," effectively discretizing the representation.
- Decoder: Reconstructs the original frames from the quantized representations.
- Straight-through estimator: A technique used during training to allow gradients to flow through the non-differentiable quantization step.
This tokenization process is crucial because it reduces the dimensionality of video data from millions of pixel values to a more manageable set of discrete tokens, making the subsequent modeling task computationally feasible.
2. Transformer Architecture
Once the video frames are tokenized, a transformer model predicts the next tokens in sequence:
- Token Embedding: Converts discrete tokens into continuous vector representations.
- Positional Encoding: Adds information about each token's position in the sequence.
- Transformer Encoder: Processes the token embeddings using self-attention mechanisms to capture dependencies between tokens.
- Output Head: Projects the transformer's output back to token probabilities for prediction.
The transformer architecture allows the model to understand complex spatial-temporal patterns within videos, capturing both short-term frame-to-frame transitions and longer-term visual narratives.
3. Dataset Handling
The custom VideoDataset class handles video processing:
- Extracts frames from video files at regular intervals.
- Applies transformations (resize, normalize) to prepare frames for the model.
- Packages frames into clips of a specified length.
4. Training Process
The training happens in two distinct phases:
- VQ-VAE Training: Optimizes the encoder, codebook, and decoder to effectively compress and reconstruct video frames while building a meaningful discrete representation.
- Transformer Training: After the VQ-VAE is trained, video frames are tokenized and fed to the transformer, which learns to predict future tokens based on past ones.
5. Video Generation
The generation process reverses the training pipeline:
- Seed frames are tokenized through the VQ-VAE encoder and quantizer.
- The transformer autoregressively generates new tokens one by one.
- These tokens are then decoded back into video frames using the VQ-VAE decoder.
Key Technical Insights
- Two-stage Architecture: Separating representation learning (VQ-VAE) from sequence modeling (transformer) makes training more stable and efficient.
- Spatial-Temporal Modeling: The model must capture both spatial relationships within frames and temporal dependencies across frames.
- Autoregressive Generation: Videos are generated one token at a time, with each new token conditioned on all previous tokens.
- Computational Efficiency: Working with discrete tokens rather than raw pixels drastically reduces the computational requirements.
This implementation demonstrates how transformer architectures, originally designed for language modeling, can be effectively adapted to video generation by incorporating appropriate tokenization strategies and handling the additional complexity of temporal data.
5.3.3 Gemini (DeepMind)
Gemini (DeepMind) is a sophisticated multimodal model that seamlessly integrates text, vision, and in some cases video within a unified architecture. Unlike earlier models that treated different data types in isolation, Gemini processes and reasons across multiple input formats simultaneously. This represents a significant advancement over previous approaches where text, images, and video were often processed by separate specialized models and then combined afterward. This unified approach allows Gemini to understand the contextual relationships between different modalities from the ground up rather than trying to merge separately processed information.
The model employs advanced cross-attention mechanisms that enable it to scale effectively across modalities. These attention mechanisms allow the model to identify relationships between elements in different formats—for example, connecting a textual description to relevant parts of an image or linking dialogue to visual events in a video sequence. This architecture enables information to flow bidirectionally between modalities, creating a more holistic understanding. Unlike simple concatenation of different input embeddings, Gemini's cross-attention system allows for dynamic weighting of information across modalities based on context and relevance, similar to how humans naturally shift focus between what they see and hear. This dynamic attention system helps the model determine which aspects of an image might be most relevant to a textual query, or conversely, which parts of a text prompt should inform the understanding of visual content.
Gemini demonstrates impressive reasoning capabilities across a wide range of multimodal inputs, including complex diagrams (such as scientific illustrations or technical schematics), video content (with temporal understanding), and multifaceted prompts that combine several input types. The model can process images at high resolution, enabling it to recognize fine details in photographs, charts, and documents.
For video analysis, Gemini can track objects over time, understand narrative progression, and even anticipate likely future developments based on visual dynamics. This capability is particularly valuable in scenarios requiring detailed visual analysis, such as interpreting medical imagery, understanding engineering diagrams, or analyzing sports footage to extract tactical insights.
This reasoning extends beyond simple recognition to include causal understanding, spatial relationships, and temporal sequences—allowing the model to answer questions like "What will happen next in this physical system?" or "How does this mechanism work?" while referencing visual material. The model's temporal understanding is crucial for tasks that involve processes unfolding over time, such as explaining chemical reactions, analyzing mechanical systems, or tracking changes in biological specimens. This capability resembles human experts' ability to "read" dynamic systems from static diagrams or limited video inputs.
Gemini's multimodal capabilities enable it to solve complex tasks requiring synthesis across modalities, such as interpreting a graph while considering textual context, explaining the steps of a visual process, or identifying inconsistencies between spoken narration and visual content. This integrated approach mirrors human cognition more closely than previous AI systems, as it can form connections between concepts across different representational formats.
This integration facilitates more natural human-AI interaction, allowing users to communicate with the system using whatever combination of text, images, or video best suits their needs, rather than being constrained to a single modality. For example, a user could ask Gemini to analyze a chart, compare it with historical data mentioned in an accompanying text, and explain apparent discrepancies—a task that requires seamless integration of visual and textual information.
Example: Gemini Implementation
import google.generativeai as genai
import PIL.Image
import os
from IPython.display import display, HTML
# Configure API key
genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
# List available models
for m in genai.list_models():
if 'generateContent' in m.supported_generation_methods:
print(m.name)
# Select Gemini Pro Vision model
model = genai.GenerativeModel('gemini-pro-vision')
# Function to analyze an image with text prompt
def analyze_image(image_path, prompt):
img = PIL.Image.open(image_path)
response = model.generate_content([prompt, img])
return response.text
# Function for multimodal reasoning with multiple images
def compare_images(image_path1, image_path2, prompt):
img1 = PIL.Image.open(image_path1)
img2 = PIL.Image.open(image_path2)
response = model.generate_content([prompt, img1, img2])
return response.text
# Example usage: Image analysis
image_analysis = analyze_image("chart.jpg",
"Analyze this chart in detail. What trends do you observe?")
print(image_analysis)
# Example usage: Image comparison
comparison = compare_images("design_v1.jpg", "design_v2.jpg",
"Compare these two design versions and explain the key differences.")
print(comparison)
# Example: Complex reasoning with image and specific instructions
reasoning = analyze_image("scientific_diagram.jpg",
"Explain how this biological process works. Focus on:
1. The starting materials
2. The transformation steps
3. The end products
4. The energy changes involved")
print(reasoning)
# Example: Video frame analysis
def analyze_video_frames(frame_paths, prompt):
frames = [PIL.Image.open(path) for path in frame_paths]
response = model.generate_content([prompt] + frames)
return response.text
frame_paths = ["video_frame1.jpg", "video_frame2.jpg", "video_frame3.jpg"]
video_analysis = analyze_video_frames(frame_paths,
"Analyze the motion sequence shown in these frames. What's happening?")
print(video_analysis)
# Safety settings example (optional)
safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_ONLY_HIGH"
}
]
model_with_safety = genai.GenerativeModel(
model_name='gemini-pro-vision',
safety_settings=safety_settings
)
Understanding the Gemini Implementation
The code example above demonstrates how to work with Google's Gemini multimodal model, providing a practical framework for integrating vision and language understanding. Let's explore the key components and capabilities:
API Configuration and Model Selection
The implementation begins by importing the necessary libraries and configuring the API with an authentication key. The code then lists available models with content generation capabilities before selecting the Gemini Pro Vision model, which is specifically designed for multimodal tasks combining text and images.
Core Functionality
The implementation provides several functions that showcase Gemini's multimodal capabilities:
- Single Image Analysis: The
analyze_image()function accepts an image path and a text prompt, then returns Gemini's interpretation of the image in the context of the prompt. This enables tasks like chart analysis, object identification, or scene description. - Comparative Image Analysis: With
compare_images(), the model can reason about relationships between multiple images, identifying similarities, differences, and patterns across visual content. This is useful for before/after comparisons, design iterations, or tracking changes. - Video Frame Analysis: Though Gemini doesn't process video directly in this implementation, the
analyze_video_frames()function demonstrates how to analyze temporal sequences by feeding multiple frames with a contextual prompt. This allows for basic motion analysis and event understanding across time.
Prompt Engineering for Multimodal Tasks
The example showcases several prompt structures that enable different types of visual reasoning:
- Open-ended analysis: "Analyze this chart in detail. What trends do you observe?" allows the model to identify and describe patterns with minimal constraints.
- Comparative analysis: "Compare these two design versions and explain the key differences" directs the model to focus specifically on contrasting visual elements.
- Structured reasoning: The scientific diagram prompt uses a numbered list to guide the model through a systematic analysis process, ensuring comprehensive coverage of specific aspects.
- Temporal understanding: "Analyze the motion sequence shown in these frames" encourages the model to consider relationships between images as representing a continuous process rather than isolated visuals.
Safety Considerations
The implementation includes optional safety settings that can be configured to control the model's outputs according to different harm categories and thresholds. This demonstrates how to implement responsible AI practices when deploying multimodal systems that might encounter or generate sensitive content.
Technical Significance
What makes this implementation particularly powerful is its simplicity relative to the complexity of the underlying model. The Gemini architecture internally handles the complex cross-attention mechanisms that align visual and textual information, allowing developers to interact with it through a straightforward API.
Unlike previous approaches that required separate models for vision and language tasks, Gemini's unified architecture enables it to process both modalities jointly, capturing the interactions between them. This is evident in how a single function call can pass both text and images to the model and receive coherent, contextually relevant responses.
Practical Applications
This implementation enables numerous real-world applications:
- Data visualization interpretation: Automatically generating insights from charts, graphs, and other visual data representations.
- Document understanding: Analyzing documents that combine text and images, such as technical manuals, academic papers, or illustrated guides.
- Educational content analysis: Processing instructional materials that use diagrams and text explanations to convey complex concepts.
- Design feedback: Providing structured analysis of visual designs, identifying issues and suggesting improvements.
- Medical image preliminary assessment: Assisting healthcare professionals by providing initial observations on medical imagery alongside clinical notes.
The example demonstrates how Gemini bridges the gap between computer vision and natural language processing, offering an integrated approach to understanding the visual world through the lens of language and vice versa.
5.3.4 Kosmos-2 (Microsoft)
Focuses on grounding language in vision, which means creating explicit connections between language descriptions and specific visual elements. This technique enables the model to understand not just what objects are in an image, but precisely where they are located and how they relate to linguistic references. The model essentially creates a detailed spatial map of the image, connecting language tokens directly to pixel regions. This grounding capability represents a fundamental shift in how AI processes visual information, moving from general scene understanding to precise object localization and reference resolution—similar to how humans point at objects while describing them. Just as a person might say "look at that red bird on the branch" while pointing, Kosmos-2 can conceptually "point" to objects it describes.
Can link words to objects in an image or video frame, enabling tasks like "point to the cat in the video." This capability represents a significant advancement over earlier models that could only describe images generally but couldn't identify specific regions or elements when prompted. For example, when asked "What is the person on the left wearing?", Kosmos-2 can both understand the spatial reference ("on the left") and ground its response to the specific person being referenced. It can generate bounding boxes or segmentation masks that highlight exactly which pixels in the image correspond to "the person on the left" before answering about their clothing. This requires sophisticated visual reasoning that combines object detection, spatial awareness, and natural language understanding in a unified framework—a computational challenge that previous generations of models struggled to address. The model must simultaneously parse language, recognize objects, understand spatial relationships, and maintain the connections between them all.
A step toward cross-modal grounding, where models tie abstract descriptions to concrete visual elements. This connection between language and vision mimics how humans naturally communicate about visual information, allowing for more precise visual reasoning, improved human-AI interaction, and the foundation for embodied AI systems that need to understand references to objects in their environment. Rather than treating language and vision as separate domains that occasionally interact, Kosmos-2 builds a shared representational space where concepts from either modality can be mapped directly to each other. The grounding capability is especially valuable for applications like visual question answering, image editing based on natural language instructions, and assistive technologies for the visually impaired. For instance, a visually impaired user could ask "Is there a cup on the table?" and receive not just a yes/no answer, but information about where exactly the cup is located relative to other objects.
By establishing direct links between words and visual regions, Kosmos-2 creates a foundation for more sophisticated reasoning tasks that require understanding both the semantics of language and the spatial configuration of visual scenes—capabilities that are essential for robots navigating physical environments, AR/VR systems responding to natural language commands about visible objects, or accessibility tools that help visually impaired users understand their surroundings through verbal descriptions. This grounding mechanism also enables multi-turn interactions about specific parts of an image, where a user might ask "What's in the corner?" followed by "What color is it?" and the model correctly maintains context about which object is being discussed. The alignment between language and vision provides a crucial building block for AI systems that must operate in the physical world, where understanding references to objects and their relationships is fundamental to meaningful interaction.
Example: Kosmos-2 Implementation
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import requests
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# Load Kosmos-2 model and processor
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
# Function to get image from URL
def get_image(url):
image = Image.open(requests.get(url, stream=True).raw)
return image
# Function to process image and generate caption with bounding boxes
def analyze_with_grounding(image, prompt="<grounding>Describe this image in detail:"):
# Process the image and text
inputs = processor(text=prompt, images=image, return_tensors="pt")
# Generate output from model
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
num_beams=5,
early_stopping=True
)
# Decode the generated text with grounding tokens
generated_text = processor.batch_decode(outputs, skip_special_tokens=False)[0]
# Extract bounding boxes from the generated output
phrase_bboxes = []
for phrase, bbox in processor.parse_bbox(generated_text):
if bbox is not None:
phrase_bboxes.append((phrase, bbox))
return generated_text, phrase_bboxes
# Function to visualize the image with bounding boxes
def visualize_with_bboxes(image, phrase_bboxes):
plt.figure(figsize=(16, 10))
plt.imshow(image)
ax = plt.gca()
# Add bounding boxes with labels
for phrase, bbox in phrase_bboxes:
x, y, width, height = bbox
rect = patches.Rectangle(
(x * image.width, y * image.height),
width * image.width,
height * image.height,
linewidth=2,
edgecolor='r',
facecolor='none'
)
ax.add_patch(rect)
plt.text(
x * image.width,
y * image.height - 5,
phrase,
color='white',
backgroundcolor='red',
fontsize=10
)
plt.axis('off')
plt.tight_layout()
plt.show()
# Function for comparing objects in an image
def compare_objects(image, prompt="<grounding>Compare the objects in this image:"):
generated_text, phrase_bboxes = analyze_with_grounding(image, prompt)
print("Generated Text:", generated_text)
visualize_with_bboxes(image, phrase_bboxes)
return generated_text, phrase_bboxes
# Function for referring expression comprehension
def find_specific_object(image, object_description):
prompt = f"<grounding>Point to the {object_description} in this image."
generated_text, phrase_bboxes = analyze_with_grounding(image, prompt)
print(f"Looking for: {object_description}")
print("Generated Text:", generated_text)
visualize_with_bboxes(image, phrase_bboxes)
return generated_text, phrase_bboxes
# Example usage
image_url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/living_room.jpg"
image = get_image(image_url)
# Basic image description with grounding
description, bboxes = analyze_with_grounding(image)
print("Description with grounding:")
print(description)
visualize_with_bboxes(image, bboxes)
# Find a specific object
find_specific_object(image, "red couch")
# Compare objects
compare_objects(image, "<grounding>Compare the furniture items in this image.")
# Spatial reasoning example
spatial_reasoning = find_specific_object(image, "lamp next to the couch")
Understanding the Kosmos-2 Implementation
The code example above demonstrates how to work with Microsoft's Kosmos-2 multimodal model, showcasing its unique capability for visual grounding. Let's break down the key components and capabilities:
Setup and Initialization
The implementation begins by importing the necessary libraries and initializing the Kosmos-2 model and processor from the Hugging Face Transformers library. Kosmos-2 is accessed through the AutoModelForVision2Seq class, which handles models that can process both vision and language.
Core Grounding Functionality
The central function analyze_with_grounding() demonstrates Kosmos-2's key innovation: the ability to connect language descriptions with specific visual elements through grounding. The function:
Processes an image along with a prompt that includes the special `` token to activate the model's grounding capabilities
- Generates a descriptive response about the image
- Extracts bounding box coordinates for objects that the model has identified and mentioned
- Returns both the generated text and a list of phrase-bounding box pairs
Visual Grounding in Action
The visualize_with_bboxes() function provides a visualization capability that overlays the model's detected objects on the original image. This visual representation shows how Kosmos-2 connects its language understanding with precise spatial locations in the image, effectively demonstrating the model's ability to "point" at objects it's describing.
Advanced Visual Reasoning Capabilities
The implementation includes specialized functions that showcase different aspects of Kosmos-2's visual reasoning abilities:
- Object Comparison: The
compare_objects()function prompts the model to identify and compare multiple objects in an image, highlighting each with bounding boxes. This demonstrates the model's ability to reason about relationships between different visual elements. - Referring Expression Comprehension: With
find_specific_object(), the model locates specific objects based on natural language descriptions. This capability is essential for tasks requiring precise object localization based on verbal instructions. - Spatial Reasoning: The example shows how Kosmos-2 can understand spatial relationships between objects (e.g., "lamp next to the couch"), combining object recognition with positional awareness.
Prompt Engineering for Grounding
The example highlights the importance of the `` token in prompts, which serves as a special instruction to the model to activate its visual grounding capabilities. Different prompting strategies demonstrate various aspects of visual reasoning:
- "Describe this image in detail" triggers comprehensive scene understanding with object localization
- "Point to the [object]" focuses the model on locating a specific item
- "Compare the objects" encourages the model to identify multiple entities and reason about their similarities and differences
Technical Significance
What makes Kosmos-2 particularly innovative is its ability to create explicit connections between natural language descriptions and specific regions in an image. Unlike earlier multimodal models that could generally describe an image but couldn't pinpoint specific objects, Kosmos-2's grounding mechanism enables:
- Precise object localization in response to natural language queries
- Fine-grained understanding of spatial relationships between objects
- The ability to answer questions about specific parts of an image
- More natural human-AI interaction by mimicking how humans point while describing
Practical Applications
This implementation of Kosmos-2 enables numerous real-world applications:
- Assistive technology: Helping visually impaired users understand their surroundings by describing specific objects and their locations
- Visual search: Finding objects in images based on natural language descriptions
- Human-robot interaction: Enabling robots to understand references to objects in their environment
- Visual question answering: Providing detailed answers about specific elements in an image
- Educational tools: Creating interactive learning experiences that connect visual concepts with language
Kosmos-2 represents an important step toward AI systems that can perceive and reason about the visual world in ways that more closely resemble human understanding, bridging the gap between seeing and communicating about what is seen.
Example: Extracting Features from Video with Hugging Face
We can’t yet run full Gemini or Kosmos in open-source, but we can use pretrained models like VideoMAE for video embeddings.
from transformers import VideoMAEFeatureExtractor, VideoMAEModel
import torch
import av # pip install av
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os
import time
def load_video_mae_model():
"""Load the pretrained VideoMAE model and feature extractor"""
print("Loading VideoMAE model...")
feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
return feature_extractor, model
def extract_frames(video_path, num_frames=8, sample_rate=30):
"""Extract frames from a video file at a specific sample rate
Args:
video_path: Path to the video file
num_frames: Maximum number of frames to extract
sample_rate: Extract every nth frame
Returns:
List of frames as numpy arrays in RGB format
"""
print(f"Extracting frames from {video_path}...")
if not os.path.exists(video_path):
raise FileNotFoundError(f"Video file not found: {video_path}")
container = av.open(video_path)
frames = []
# Get video info
video_stream = container.streams.video[0]
fps = video_stream.average_rate
duration = container.duration / 1000000 # in seconds
total_frames = video_stream.frames
print(f"Video info: {fps} fps, {duration:.2f}s duration, {total_frames} total frames")
start_time = time.time()
for i, frame in enumerate(container.decode(video=0)):
if i % sample_rate == 0:
frames.append(frame.to_ndarray(format="rgb24"))
print(f"Extracted frame {len(frames)}/{num_frames} (video position: {i})")
if len(frames) == num_frames:
break
process_time = time.time() - start_time
print(f"Frame extraction complete. Extracted {len(frames)} frames in {process_time:.2f}s")
return frames
def get_video_embeddings(feature_extractor, model, frames):
"""Process frames and extract embeddings using VideoMAE
Args:
feature_extractor: VideoMAE feature extractor
model: VideoMAE model
frames: List of video frames as numpy arrays
Returns:
Video embeddings tensor and raw model outputs
"""
if len(frames) == 0:
raise ValueError("No frames were extracted from the video")
print(f"Processing {len(frames)} frames with VideoMAE...")
# Preprocess frames
inputs = feature_extractor(frames, return_tensors="pt")
# Extract embeddings
with torch.no_grad():
outputs = model(**inputs)
video_embeddings = outputs.last_hidden_state
return video_embeddings, outputs
def visualize_frames_and_embeddings(frames, embeddings):
"""Visualize extracted frames and a 2D PCA projection of their embeddings"""
# Visualize frames
num_frames = len(frames)
fig, axes = plt.subplots(1, num_frames, figsize=(16, 4))
for i, (frame, ax) in enumerate(zip(frames, axes)):
ax.imshow(frame)
ax.set_title(f"Frame {i}")
ax.axis('off')
plt.tight_layout()
plt.savefig("video_frames.png")
plt.show()
# Visualize embedding patterns (simple 2D visualization)
# Take mean over tokens dimension to get per-frame representations
frame_embeddings = embeddings.mean(dim=1).squeeze(0)
# PCA-like dimensionality reduction (simplified)
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
reduced_embeddings = pca.fit_transform(frame_embeddings.numpy())
plt.figure(figsize=(8, 6))
plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1])
# Add frame numbers
for i, (x, y) in enumerate(reduced_embeddings):
plt.annotate(str(i), (x, y), fontsize=12)
plt.title("2D projection of frame embeddings")
plt.xlabel("Component 1")
plt.ylabel("Component 2")
plt.grid(True, linestyle='--', alpha=0.7)
plt.savefig("embedding_visualization.png")
plt.show()
def compute_frame_similarity(embeddings):
"""Compute cosine similarity between frame embeddings"""
# Mean over token dimension to get per-frame embeddings
frame_embeddings = embeddings.mean(dim=1).squeeze(0)
# Normalize embeddings
norm = frame_embeddings.norm(dim=1, keepdim=True)
normalized_embeddings = frame_embeddings / norm
# Compute similarity matrix
similarity = torch.mm(normalized_embeddings, normalized_embeddings.t())
# Visualize similarity matrix
plt.figure(figsize=(8, 6))
plt.imshow(similarity.numpy(), cmap='viridis')
plt.colorbar(label='Cosine Similarity')
plt.title("Frame-to-Frame Similarity")
plt.xlabel("Frame Index")
plt.ylabel("Frame Index")
plt.savefig("frame_similarity.png")
plt.show()
return similarity
def detect_scene_changes(similarity_matrix, threshold=0.8):
"""Simple scene change detection based on frame similarity"""
# Check if adjacent frames are below similarity threshold
scene_changes = []
sim_np = similarity_matrix.numpy()
for i in range(len(sim_np) - 1):
if sim_np[i, i+1] < threshold:
scene_changes.append(i+1)
print(f"Detected {len(scene_changes)} potential scene changes at frames: {scene_changes}")
return scene_changes
def main():
# Load model
feature_extractor, model = load_video_mae_model()
# Process video
video_path = "sample_video.mp4"
frames = extract_frames(video_path, num_frames=8, sample_rate=30)
# Get embeddings
video_embeddings, outputs = get_video_embeddings(feature_extractor, model, frames)
print("Video embeddings shape:", video_embeddings.shape) # [batch, frames, hidden_dim]
# Visualize frames and embeddings
visualize_frames_and_embeddings(frames, video_embeddings)
# Compute and visualize frame similarity
similarity = compute_frame_similarity(video_embeddings)
# Detect scene changes
scene_changes = detect_scene_changes(similarity, threshold=0.8)
print("Processing complete!")
if __name__ == "__main__":
main()
The example above demonstrates a comprehensive approach to working with videos in machine learning contexts using the VideoMAE (Video Masked Autoencoder) model. VideoMAE is a self-supervised learning framework for video understanding that works by reconstructing masked portions of video frames. Let's break down the key components:
Video Frame Extraction: The code uses the PyAV library to efficiently decode and extract frames from video files at specified intervals. This is crucial for video processing since working with every frame would be computationally expensive and often redundant, as adjacent frames typically contain similar information.
Feature Extraction with VideoMAE: The extracted frames are processed through VideoMAE, which transforms the raw pixel data into high-dimensional feature vectors (embeddings). These embeddings capture semantic information about objects, actions, and scenes present in the video.
Visualization Components: The code includes several visualization functions that help understand both the raw video content (displaying extracted frames) and the encoded representations (embedding visualizations). This is valuable for debugging and gaining insights into how the model "sees" the video.
Frame Similarity Analysis: By computing cosine similarity between frame embeddings, the code can identify how similar or different consecutive frames are. This has practical applications in scene boundary detection, content summarization, and keyframe extraction.
Scene Change Detection: A simple threshold-based approach is implemented to detect potential scene changes, which could be useful for video indexing, summarization, or creating chapter markers.
The code represents a foundation for more complex video understanding tasks like action recognition, video captioning, or video question answering. These capabilities are essential for applications ranging from content moderation and video search to assistive technologies for the visually impaired.
When working with the VideoMAE model, it's important to understand that:
- The model's input preprocessing is specific and requires frames to be in a particular format and dimension.
- The output embeddings are hierarchical and capture different levels of temporal and spatial information.
- The token dimension in the output shape represents the spatial tokens that the image is divided into.
- For downstream tasks, you would typically need to apply additional processing or fine-tuning to adapt these generic embeddings for specific purposes.
This code example provides a solid starting point for exploring multimodal capabilities that bridge the gap between computer vision and natural language processing, which is increasingly important as AI systems need to understand the world in ways that more closely resemble human perception.
5.3.5 Cross-Modal Reasoning
Cross-modal reasoning goes beyond processing modalities in isolation. It's about integration - the ability to synthesize and analyze information across different perceptual channels simultaneously. This represents a significant advancement over systems that can only process one type of input at a time, as it mirrors how humans naturally perceive and understand the world around them.
Unlike traditional AI systems that handle each input type separately, cross-modal models create a unified understanding by establishing connections between different types of information, enabling more comprehensive and contextual analysis. This integration happens at a deep representational level, where the model learns to map concepts across modalities into a shared semantic space.
For example, when a cross-modal system processes both an image of a dog and the word "dog," it doesn't treat these as separate, unrelated inputs. Instead, it recognizes they refer to the same concept despite coming through different perceptual channels. This ability to form these cross-modal associations is fundamental to human cognition and represents a crucial step toward more human-like AI understanding.
The technical implementation of cross-modal reasoning often involves complex neural architectures with shared embedding spaces, cross-attention mechanisms, and fusion techniques that preserve the unique characteristics of each modality while enabling information to flow between them. These systems must learn not just to process each modality effectively but to identify meaningful correlations between them, distinguishing relevant connections from coincidental ones.
Audio + Video:
Lip-reading and voice alignment, where models can match spoken words with mouth movements to improve speech recognition in noisy environments or for hearing-impaired users. This integration allows for more robust communication understanding. The system analyzes both the visual cues of lip movements and the acoustic properties of speech, compensating for deficiencies in either modality.
When processing lip movements, these systems track facial landmarks and mouth shapes that correspond to specific phonemes (speech sounds). Meanwhile, the audio component analyzes spectral and temporal features of the speech signal. By combining these streams of information, the system can disambiguate similar-sounding phonemes that have distinct visual representations (like "ba" vs "fa") or clarify unclear audio by leveraging the visual channel.
Advanced models employ attention mechanisms that dynamically weight the importance of visual versus audio inputs depending on their reliability. For instance, when ambient noise increases, the system automatically places greater emphasis on visual information. Conversely, in low-light conditions where visual data is less reliable, the audio channel receives higher priority.
This is particularly valuable in crowded settings where background noise might otherwise make speech recognition impossible, or in assistive technologies for people with hearing impairments who rely partly on visual cues for communication. In teleconferencing applications, this technology helps maintain clear communication even with unstable internet connections by reconstructing parts of the message from the available modality.
Example: Cross-Modal Reasoning: Audio + Video Integration
import torch
import torchaudio
import torch.nn as nn
import torch.nn.functional as F
import av
import numpy as np
import matplotlib.pyplot as plt
from transformers import Wav2Vec2Processor, Wav2Vec2Model
from transformers import VideoMAEFeatureExtractor, VideoMAEModel
from sklearn.metrics.pairwise import cosine_similarity
from PIL import Image
import librosa
import librosa.display
class AudioVideoSyncModel(nn.Module):
"""
A model for audio-video synchronization and cross-modal reasoning
"""
def __init__(self, audio_dim=768, video_dim=768, joint_dim=512):
super().__init__()
self.audio_projection = nn.Linear(audio_dim, joint_dim)
self.video_projection = nn.Linear(video_dim, joint_dim)
self.cross_attention = nn.MultiheadAttention(
embed_dim=joint_dim,
num_heads=8,
batch_first=True
)
self.classifier = nn.Sequential(
nn.Linear(joint_dim, 256),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(256, 1),
nn.Sigmoid()
)
def forward(self, audio_features, video_features):
"""
Process audio and video features and compute synchronization score
Args:
audio_features: Tensor of shape [batch_size, seq_len_audio, audio_dim]
video_features: Tensor of shape [batch_size, seq_len_video, video_dim]
Returns:
sync_score: Synchronization probability between 0-1
joint_features: Cross-modal features after attention
"""
# Project to common space
audio_proj = self.audio_projection(audio_features)
video_proj = self.video_projection(video_features)
# Apply cross-attention from video to audio
joint_features, _ = self.cross_attention(
query=video_proj,
key=audio_proj,
value=audio_proj
)
# Get global representation by mean pooling
global_joint = torch.mean(joint_features, dim=1)
# Predict synchronization score
sync_score = self.classifier(global_joint)
return sync_score, joint_features
def extract_video_frames(video_path, sample_rate=5):
"""
Extract frames from a video at regular intervals
Args:
video_path: Path to video file
sample_rate: Sample every nth frame
Returns:
List of frames as numpy arrays in RGB format
"""
frames = []
try:
container = av.open(video_path)
stream = container.streams.video[0]
total_frames = stream.frames
fps = float(stream.average_rate)
print(f"Video: {total_frames} frames, {fps} fps")
for i, frame in enumerate(container.decode(video=0)):
if i % sample_rate == 0:
# Convert to RGB numpy array
img = frame.to_ndarray(format='rgb24')
frames.append(img)
print(f"Extracted {len(frames)} frames")
container.close()
except Exception as e:
print(f"Error extracting video frames: {e}")
return frames
def extract_audio_from_video(video_path, target_sr=16000):
"""
Extract audio from a video file
Args:
video_path: Path to video file
target_sr: Target sampling rate
Returns:
Audio waveform and sample rate
"""
try:
container = av.open(video_path)
audio_stream = container.streams.audio[0]
# Initialize an empty numpy array to store audio samples
audio_data = []
# Decode audio
for frame in container.decode(audio=0):
# Convert PyAV AudioFrame to numpy array
frame_data = frame.to_ndarray()
audio_data.append(frame_data)
# Concatenate audio frames
if audio_data:
audio_array = np.concatenate(audio_data)
# Convert to mono if stereo
if len(audio_array.shape) > 1 and audio_array.shape[1] > 1:
audio_array = np.mean(audio_array, axis=1)
# Resample if needed
original_sr = audio_stream.rate
if original_sr != target_sr:
audio_resampled = librosa.resample(
audio_array,
orig_sr=original_sr,
target_sr=target_sr
)
return audio_resampled, target_sr
return audio_array, original_sr
else:
raise ValueError("No audio frames found")
except Exception as e:
print(f"Error extracting audio: {e}")
return None, None
def process_video(video_path, video_model, video_processor, sample_rate=5):
"""
Extract and process video frames
Args:
video_path: Path to video file
video_model: VideoMAE model
video_processor: VideoMAE feature extractor
sample_rate: Sample every nth frame
Returns:
Video features tensor
"""
# Extract frames
frames = extract_video_frames(video_path, sample_rate)
if not frames:
raise ValueError("No frames were extracted")
# Process frames with VideoMAE
inputs = video_processor(frames, return_tensors="pt")
with torch.no_grad():
outputs = video_model(**inputs)
video_features = outputs.last_hidden_state
return video_features, frames
def process_audio(audio_array, sr, audio_model, audio_processor):
"""
Process audio with Wav2Vec2
Args:
audio_array: Audio samples as numpy array
sr: Sample rate
audio_model: Wav2Vec2 model
audio_processor: Wav2Vec2 processor
Returns:
Audio features tensor
"""
# Prepare audio for Wav2Vec2
inputs = audio_processor(
audio_array,
sampling_rate=sr,
return_tensors="pt"
)
with torch.no_grad():
outputs = audio_model(**inputs)
audio_features = outputs.last_hidden_state
return audio_features
def detect_audiovisual_sync(sync_scores, threshold=0.5):
"""
Analyze synchronization scores to detect in-sync vs out-of-sync segments
Args:
sync_scores: List of synchronization scores
threshold: Threshold for considering audio-video in sync
Returns:
List of in-sync and out-of-sync segments
"""
segments = []
current_segment = {"start": 0, "status": "sync" if sync_scores[0] >= threshold else "out-of-sync"}
for i in range(1, len(sync_scores)):
current_status = "sync" if sync_scores[i] >= threshold else "out-of-sync"
previous_status = "sync" if sync_scores[i-1] >= threshold else "out-of-sync"
if current_status != previous_status:
# End the previous segment
current_segment["end"] = i - 1
segments.append(current_segment)
# Start a new segment
current_segment = {"start": i, "status": current_status}
# Add the final segment
current_segment["end"] = len(sync_scores) - 1
segments.append(current_segment)
return segments
def visualize_sync_analysis(frames, audio_waveform, sr, sync_scores, segments):
"""
Visualize audio, video frames, and synchronization analysis
Args:
frames: List of video frames
audio_waveform: Audio samples
sr: Audio sample rate
sync_scores: Synchronization scores
segments: Detected sync/out-of-sync segments
"""
fig, axes = plt.subplots(3, 1, figsize=(15, 10), gridspec_kw={'height_ratios': [1, 1, 2]})
# Plot synchronization scores
axes[0].plot(sync_scores)
axes[0].set_ylim(0, 1)
axes[0].set_ylabel('Sync Score')
axes[0].set_xlabel('Frame')
axes[0].axhline(y=0.5, color='r', linestyle='--')
# Highlight sync/out-of-sync segments
for segment in segments:
color = 'green' if segment['status'] == 'sync' else 'red'
axes[0].axvspan(segment['start'], segment['end'], alpha=0.2, color=color)
# Plot audio waveform
librosa.display.waveshow(audio_waveform, sr=sr, ax=axes[1])
axes[1].set_ylabel('Amplitude')
# Display frames at key points
n_frames = min(8, len(frames))
indices = np.linspace(0, len(frames)-1, n_frames, dtype=int)
for i, idx in enumerate(indices):
ax = plt.subplot(3, n_frames, i + 2*n_frames + 1)
ax.imshow(frames[idx])
ax.set_title(f"Frame {idx}")
ax.axis('off')
plt.tight_layout()
plt.savefig('av_sync_analysis.png')
plt.show()
def demonstrate_lip_reading(sync_model, audio_features, video_features, frames):
"""
Demonstrate lip-reading by finding the most relevant audio for each video frame
using the cross-attention mechanism
Args:
sync_model: Trained AudioVideoSyncModel
audio_features: Audio features tensor
video_features: Video features tensor
frames: List of video frames
Returns:
Attention weights showing audio-visual connections
"""
# Project features to common space
audio_proj = sync_model.audio_projection(audio_features)
video_proj = sync_model.video_projection(video_features)
# Compute raw attention scores
attn_weights = torch.matmul(video_proj, audio_proj.transpose(-2, -1)) / np.sqrt(audio_proj.size(-1))
# Convert to probabilities
attn_probs = F.softmax(attn_weights, dim=-1)
# Visualize attention for selected frames
n_frames = min(4, len(frames))
indices = np.linspace(0, len(frames)-1, n_frames, dtype=int)
fig, axes = plt.subplots(2, n_frames, figsize=(15, 6))
for i, idx in enumerate(indices):
# Show the frame
axes[0, i].imshow(frames[idx])
axes[0, i].set_title(f"Frame {idx}")
axes[0, i].axis('off')
# Show attention weights (which audio segments this frame attends to)
if idx < attn_probs.shape[1]: # Ensure index is valid
axes[1, i].plot(attn_probs[0, idx].detach().numpy())
axes[1, i].set_title("Audio Attention")
axes[1, i].set_xlabel("Audio Frames")
axes[1, i].set_ylabel("Attention Weight")
plt.tight_layout()
plt.savefig('lip_reading_attention.png')
plt.show()
return attn_probs
def main():
# Initialize models
print("Loading models...")
# Audio model (Wav2Vec2)
audio_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
audio_model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
# Video model (VideoMAE)
video_processor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
video_model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
# Cross-modal synchronization model
sync_model = AudioVideoSyncModel(
audio_dim=768, # Wav2Vec2 feature dimension
video_dim=768, # VideoMAE feature dimension
joint_dim=512 # Joint embedding dimension
)
# Process video with speaking person
video_path = "speaking_person.mp4"
print(f"Processing video: {video_path}")
# Extract audio from video
audio_array, sr = extract_audio_from_video(video_path)
if audio_array is None:
print("Failed to extract audio")
return
print(f"Audio: {len(audio_array)} samples, {sr} Hz")
# Process video frames
video_features, frames = process_video(
video_path,
video_model,
video_processor,
sample_rate=5
)
# Process audio
audio_features = process_audio(
audio_array,
sr,
audio_model,
audio_processor
)
print("Audio features shape:", audio_features.shape)
print("Video features shape:", video_features.shape)
# Simulate training the sync model (in practice, this requires proper training)
# Here we're just demonstrating the forward pass
sync_scores = []
step_size = max(1, audio_features.shape[1] // video_features.shape[1])
for i in range(video_features.shape[1]):
# Get corresponding audio chunk
start_idx = i * step_size
end_idx = min((i + 1) * step_size, audio_features.shape[1])
audio_chunk = audio_features[:, start_idx:end_idx, :]
video_frame_feat = video_features[:, i:i+1, :]
# Mean pool audio chunk
audio_chunk_pooled = torch.mean(audio_chunk, dim=1, keepdim=True)
# Get sync score
score, _ = sync_model(audio_chunk_pooled, video_frame_feat)
sync_scores.append(score.item())
# Analyze synchronization
segments = detect_audiovisual_sync(sync_scores)
print("Detected segments:")
for segment in segments:
print(f"Frames {segment['start']}-{segment['end']}: {segment['status']}")
# Visualize results
visualize_sync_analysis(frames, audio_array, sr, sync_scores, segments)
# Demonstrate lip reading capabilities
print("Generating lip reading visualization...")
attn_weights = demonstrate_lip_reading(sync_model, audio_features, video_features, frames)
print("Analysis complete!")
if __name__ == "__main__":
main()
This code example demonstrates a comprehensive approach to cross-modal reasoning with audio and video, focusing on lip-reading and speech-video synchronization analysis. Let's break down the key components:
The AudioVideoSyncModel class implements a neural network architecture that processes both audio and video features and learns to align them in a shared representation space. It uses several important mechanisms:
- Modal-specific projection layers that map audio and video features to a common semantic space
- Cross-attention mechanisms that allow the model to determine which parts of the audio correspond to which visual frames
- A classification head that predicts whether audio and video are synchronized
The extract_video_frames function extracts frames from a video at regular intervals using PyAV, which provides a Pythonic binding to the FFmpeg libraries. This sampling approach is essential for efficiency since processing every frame would be computationally expensive and often redundant for semantic understanding.
Similarly, extract_audio_from_video extracts the audio track from a video file and processes it into a format suitable for deep learning models, including converting to a consistent sampling rate and handling multi-channel audio.
The process_video and process_audio functions use pretrained models from the Transformers library to convert raw video frames and audio signals into high-dimensional feature representations:
- VideoMAE (Video Masked Autoencoder) processes video frames, extracting features that capture objects, actions, and visual context
- Wav2Vec2 processes audio, capturing phonetic and linguistic information
The detect_audiovisual_sync function analyzes the synchronization scores to identify segments where audio and video are well-aligned versus segments where they might be out of sync. This is valuable for applications like automatic correction of audio-video synchronization issues in recorded content.
The visualize_sync_analysis function creates a comprehensive visualization showing:
- The synchronization scores over time
- Color-coded segments indicating in-sync and out-of-sync portions
- The audio waveform
- Key video frames from throughout the sequence
The demonstrate_lip_reading function shows how the cross-attention mechanism effectively implements a form of lip reading by connecting mouth movements in video frames with corresponding audio segments. It visualizes the attention weights, showing which parts of the audio each video frame is most strongly associated with.
In the main function, we see the entire pipeline in action:
- Models are loaded and initialized
- Video and audio are extracted and processed
- The synchronization model is applied to analyze the alignment between modalities
- Results are visualized for interpretation
This implementation has numerous practical applications:
- Assistive technology for hearing-impaired users to enhance speech understanding with visual cues
- Video production tools that automatically detect and correct audio-video synchronization issues
- Enhanced speech recognition in noisy environments by leveraging visual information
- Security applications for detecting manipulated content where audio and video don't naturally align
- Educational tools that ensure properly synchronized content for optimal learning experiences
The example represents a foundation that could be extended with more sophisticated training procedures and architectural improvements. In a production environment, this system would require proper training data consisting of paired audio-video examples with both synchronized and deliberately misaligned samples.
Text + Image
Answering questions about a chart or photo, which requires understanding both the visual elements (colors, shapes, spatial relationships) and textual context to provide meaningful responses. This capability enables more intuitive data exploration and visual information retrieval. The model must recognize visual patterns, understand spatial arrangements, and interpret color encodings while simultaneously processing textual labels and contextual information provided alongside the image. This visual-textual integration requires sophisticated neural architectures that can maintain representations from both modalities and reason across them effectively.
For example, when analyzing a financial chart, the system must understand not only the visual representation of data trends but also interpret labels, legends, and axes to provide accurate insights. It needs to recognize different chart types (bar charts, line graphs, pie charts), understand what each visual element represents (rising trends, market segments, comparative data), and correctly interpret numerical scales and time periods. The system must also discern the significance of color coding (e.g., red for losses, green for gains) and pattern variations (e.g., dotted lines for projections versus solid lines for historical data), while connecting these visual cues to financial terminology and concepts in the accompanying text.
Similarly, in medical imaging, a cross-modal system can correlate visual patterns in scans with textual patient records to assist in diagnosis or treatment planning. This requires identifying subtle visual anomalies in X-rays, MRIs, or CT scans while simultaneously considering patient history, symptoms, and other clinical notes to provide contextually relevant medical analysis. The system must recognize anatomical structures, detect abnormalities like fractures, tumors, or inflammation, and understand how these visual findings relate to symptoms described in textual records. This integration enables more comprehensive clinical decision support by connecting what is seen in the image with what is known about the patient's condition.
This integration of visual and textual information also extends to other domains like geospatial analysis (interpreting maps alongside location descriptions), document understanding (processing diagrams with explanatory text), and educational content (connecting visual teaching aids with textual explanations). In geospatial applications, models must understand geographical features, topographical elements, and symbolic representations on maps while relating them to textual location descriptions, directions, or demographic data. For document understanding, the system needs to parse complex layouts with mixed text and visuals, comprehending how diagrams illustrate concepts explained in accompanying text.
The true power of multimodal systems emerges when they can seamlessly blend these different information streams into a unified understanding, allowing for more natural human-AI interaction across diverse applications. This unified comprehension enables AI systems to provide more contextually appropriate responses that consider the full range of available information, similar to how humans integrate multiple sensory inputs when understanding their environment. Through techniques like cross-attention and joint embedding spaces, these models create rich representations that capture the relationships between words and visual elements, enabling more sophisticated reasoning that mirrors human cognitive processes.
Text + Video
Explaining an event in a clip or summarizing a documentary, which demands temporal reasoning across frames while connecting visual elements to narrative structure. This integration supports content analysis at a much deeper semantic level than static image understanding, as it requires processing sequential information and understanding how scenes evolve over time. The AI must analyze multiple dimensions simultaneously - visual composition, motion patterns, temporal transitions, audio cues, and narrative progression - to construct a coherent understanding of the content.
The system must track objects and actors across time, understand causality between events, and connect visual sequences with contextual information. This tracking involves sophisticated computer vision algorithms that can maintain object identity despite changes in appearance, lighting, camera angle, or partial occlusion. For example, when analyzing a nature documentary, the model needs to recognize not just individual animals, but follow their movements across different shots, understand the narrative arc (such as a predator stalking prey), and connect these visual sequences with the documentary's educational themes. The system must interpret both explicit visual information (what is directly shown) and implicit content (what is suggested or implied through editing techniques, camera movements, or juxtaposition of scenes).
Temporal reasoning also requires understanding cinematic language - how cuts, transitions, establishing shots, close-ups, and montages contribute to storytelling. The model must recognize when a flashback occurs, when parallel storylines are being presented, or when a montage compresses time. Similarly, for news footage, the system must recognize key figures, understand the chronology of events, and place them within the broader context provided by narration or interviews. This involves correlating spoken information with visual evidence, distinguishing between primary footage and archival material, and recognizing when the same event is shown from multiple perspectives.
This multimodal reasoning enables applications like automatically generating detailed video summaries that capture both visual content and narrative structure, creating accessible descriptions for visually impaired users that convey the emotional and storytelling elements of video content, or analyzing surveillance footage with textual reports to identify specific incidents by matching visual patterns with textual descriptions of events. These applications require not just object recognition but scene understanding - comprehending the relationships between objects, their interactions, and how these elements combine to create meaning.
Advanced systems can even identify emotional arcs in film by correlating visual cinematography techniques with dialogue and music to understand how directors convey meaning through multiple channels simultaneously. This includes analyzing color grading (how warm or cool tones evoke different emotions), camera movement (steady vs. handheld to convey stability or tension), lighting techniques (high-key vs. low-key for different moods), and how these visual elements synchronize with musical cues, sound effects, and dialogue to create a unified emotional experience.
The ultimate goal is to develop AI systems that can "watch" and "understand" video content with a level of comprehension approaching that of human viewers, interpreting both denotative content (what is literally shown) and connotative meaning (what is symbolically or emotionally conveyed).
Example Use Cases:
- Accessibility: automatic captioning of lectures that combines audio transcription with slide descriptions, making educational content more accessible to people with hearing impairments or those learning in noisy environments. The system must synchronize the verbal explanation with relevant visual content.
This requires sophisticated speech recognition that can handle technical terminology and different accents, while also identifying the context of what's being discussed by analyzing visual slides. The technology must accurately timestamp speech to align with corresponding visual elements, creating a seamless experience that mimics how in-person attendees process the lecture.
- Education: tutoring systems that can explain a diagram while narrating, creating more engaging and comprehensive learning experiences by linking visual concepts with verbal explanations. These systems can adapt to different learning styles and provide multimodal reinforcement of complex concepts.
For example, when teaching molecular biology, the system could highlight specific parts of a cell diagram while verbally explaining their functions, then dynamically adjust its teaching approach based on student comprehension signals. This multimodal approach helps students form stronger mental models by connecting abstract concepts with visual representations, significantly enhancing knowledge retention compared to single-mode instruction.
- Robotics: interpreting both visual signals and verbal instructions simultaneously, enabling more natural human-robot interaction in collaborative environments. This allows robots to understand contextual commands like "pick up the red cup on the left" by combining vision processing with language understanding.
This integration is critical for assistive robots in healthcare, manufacturing, and household settings, where they must navigate complex, dynamic environments while responding to human directives that reference objects in physical space. Advanced systems can also interpret human gestures, facial expressions, and environmental cues alongside verbal commands, creating more intuitive and efficient human-robot collaboration that doesn't require humans to adapt their natural communication style.
5.3.6 Why This Matters
Video adds the dimension of time, allowing AI to model cause and effect. This temporal dimension enables AI systems to understand sequences of events, track objects through space, and recognize patterns that unfold over time. Unlike static images, video provides context about how actions lead to consequences, how objects interact, and how scenes transform. When processing video, AI can analyze motion trajectories, temporal correlations, and dynamic changes that reveal deeper insights about physical phenomena and behavioral patterns.
This capability is crucial for applications like autonomous driving (predicting pedestrian movements based on gait patterns and historical trajectory), security systems (detecting unusual behavior patterns by comparing current activities against established norms), and healthcare (analyzing patient movements in physical therapy to assess recovery progress and provide real-time feedback). The temporal reasoning enabled by video analysis allows AI to understand not just what is happening in a single moment, but how events unfold over time, creating a more complete understanding of complex scenarios.
By processing multiple frames in sequence, AI can learn to anticipate what might happen next based on what it has observed, similar to how humans develop intuitive physics. This predictive capability stems from the model's ability to extract temporal dependencies between consecutive frames, identifying cause-effect relationships and recurring patterns.
For example, in sports analysis, AI can predict player movements based on historical behavior, while in weather forecasting, it can identify evolving cloud formations that indicate changing weather conditions. This temporal understanding is fundamental to creating AI systems that can interact meaningfully with our dynamic world.
Cross-modal reasoning allows AI to integrate multiple senses, mirroring human perception. Just as humans simultaneously process what they see, hear, and read to form a complete understanding of their environment, multimodal AI systems can correlate information across different input types. This integration enables more robust understanding - when one modality provides unclear information, others can compensate. This capability represents a fundamental shift from traditional AI systems that process each sensory input independently to a more holistic approach that considers the relationships and interdependencies between different forms of information.
The power of cross-modal reasoning lies in its ability to leverage complementary information from different sources, similar to how humans instinctively combine multiple sensory inputs to navigate complex environments. By establishing correlations between visual patterns, auditory signals, and textual descriptions, AI systems can develop a more nuanced understanding of the world that transcends the limitations of any single modality. This approach allows the system to be more resilient to noise or ambiguity in individual channels by drawing on the strengths of other available inputs.
For example, in noisy environments, visual lip reading can enhance speech recognition, while in visually complex scenes, audio cues can help identify important elements. In clinical settings, AI systems can correlate medical images with written patient histories and verbal descriptions from healthcare providers to form more comprehensive diagnostic assessments. During video conference analysis, the system can integrate facial expressions, voice tone, and textual chat to better understand participant engagement and emotional states.
This cross-modal reasoning also allows AI to understand concepts more deeply by connecting abstract descriptions (text) with concrete sensory experiences (images, sounds), creating richer mental representations that more closely resemble human understanding. When an AI system can connect the textual description of "rustling leaves" with both visual imagery of moving foliage and the corresponding audio, it develops a more complete conceptual understanding than would be possible through any single modality alone. This multi-dimensional representation enables more sophisticated reasoning about real-world scenarios and more intuitive interaction with human users.
Together, these directions push LLMs closer to being general AI assistants, not just text predictors. By expanding beyond text-only processing, these systems can interact with the world more naturally and comprehensively. They can analyze and discuss visual content, process information that unfolds over time, understand speech in context, and integrate these diverse inputs into coherent responses.
This broader perception allows AI assistants to handle tasks that require understanding the physical world - from helping visually impaired users navigate environments to assisting professionals in analyzing complex multimodal data like medical scans with patient histories. The evolution toward true multimodal understanding represents a significant step toward AI systems that can perceive and reason about the world in ways that more closely align with human cognitive capabilities.
5.3.7 Looking Ahead
The frontier of multimodal AI is moving toward true integration: models that seamlessly blend text, vision, audio, and video in a single framework. This represents a significant evolution beyond current approaches where separate models handle different modalities or where models specialize in specific pairings like text-image or audio-text. True integration means developing neural architectures that process all modalities simultaneously through shared attention mechanisms and unified embedding spaces, allowing information to flow freely across different sensory channels.
These integrated models can process multiple input streams in parallel while maintaining awareness of how they relate to each other contextually. For example, understanding that a speaker's gestures on video correspond to specific concepts mentioned in their speech, or that a diagram shown in a presentation directly illustrates a verbal explanation. This cross-modal attention enables much richer understanding than processing each stream independently.
Instead of switching between specialized systems, one unified model could seamlessly process and analyze multiple forms of content simultaneously, providing a truly integrated understanding:
- Watch a video lecture, tracking visual demonstrations, facial expressions, and board work. This visual processing would include recognizing the instructor's gestures that emphasize key points, identifying when they're directing attention to specific areas, and understanding visual demonstrations that illustrate complex concepts. The model would also track changes on boards or screens, understanding how written content evolves over time.
- Listen to the narration, including tonal emphasis, pauses, and verbal cues that signal important concepts. This audio processing would detect changes in vocal pitch and volume that indicate emphasis, recognize rhetorical questions versus literal ones, understand when pauses signal transitions between topics, and identify verbal markers like "importantly" or "remember this" that highlight critical information.
- Read the slides, processing textual content, diagrams, charts, and their spatial relationships. This would involve understanding how bullet points relate hierarchically, interpreting complex visualizations like flowcharts or graphs, recognizing when text labels correspond to visual elements, and comprehending how the spatial arrangement of information conveys structural relationships between concepts.
- Summarize everything in plain English, integrating insights from all modalities into a coherent narrative. This would combine information from all sources, resolving conflicts when different modalities present contradictory information, prioritizing content based on emphasis across modalities, and presenting a unified understanding that captures the essential knowledge from all sources in a human-readable format.
These capabilities go far beyond simple feature extraction from different modalities. They represent a fundamental shift in how AI systems process and integrate information across sensory channels. While traditional multimodal systems might separately process text, images, and audio before combining their outputs, truly integrated multimodal models employ sophisticated cross-attention mechanisms that allow information to flow bidirectionally between modalities throughout the entire processing pipeline.
These cross-attention mechanisms enable several critical functions: They can dynamically align corresponding elements across modalities (matching spoken words with relevant visual objects), establish semantic connections between different representations of the same concept (connecting the word "dog" with both its visual appearance and the sound of barking), and detect discrepancies when information from different modalities appears contradictory (recognizing when spoken instructions conflict with visual demonstrations).
The resolution of these complex relationships into a unified understanding requires models to develop abstract representations that capture meaning independently of the source modality. This allows the system to identify when information in one modality complements, reinforces, or contradicts information in another, and to make reasoned judgments about how to integrate these various inputs.
For instance, when a lecturer says "as you can see in this graph" while pointing to a chart, the model must perform a complex series of operations: it must process the audio to extract the verbal reference, track the physical gesture through visual processing, identify the chart as the object being referenced, analyze the chart's content, and then integrate all this information into a coherent semantic representation that connects the verbal explanation with the visual data. This requires temporal alignment (matching when words are spoken with when gestures occur), spatial alignment (connecting the gesture to the specific area of the chart), and semantic alignment (understanding how the spoken explanation relates to the visual information).
These are the kinds of sophisticated capabilities being pioneered in research labs today through several innovative approaches:
Multiway transformers that process different modalities in parallel while allowing attention to flow between them, enabling each modality to influence how others are processed. These architectures extend the traditional transformer design by implementing specialized encoding pathways for each modality (text, image, audio, video) while maintaining cross-modal attention mechanisms. For example, when processing a video lecture, the visual pathway might attend to important visual elements while simultaneously receiving attention signals from the audio pathway that processes the speaker's voice, creating a dynamic feedback loop between modalities.
Shared embedding spaces that map inputs from different modalities into a common representational format where relationships between concepts can be directly compared regardless of their source. These unified semantic spaces enable the model to recognize that the word "apple," an image of an apple, and the sound of someone biting into an apple all refer to the same underlying concept. This approach creates a language-agnostic representation that captures meaning beyond the surface-level characteristics of any particular modality, allowing the model to transfer knowledge across modalities and reason about concepts at an abstract level.
Contrastive learning techniques that teach models to recognize when different modal representations refer to the same underlying concept by bringing their embeddings closer together in the shared space. These methods work by training the model to minimize the distance between representations of semantically related inputs (like an image of a dog and the text "a golden retriever playing") while maximizing the distance between unrelated inputs. Advanced implementations use techniques like CLIP (Contrastive Language-Image Pre-training), which learns powerful visual representations by training on millions of image-text pairs, enabling zero-shot recognition of visual concepts based on their textual descriptions.
These approaches are further enhanced by techniques like cross-modal attention masking (selectively focusing on relevant parts of each modality), modality-specific preprocessing layers (handling the unique characteristics of each input type), and sophisticated alignment strategies that synchronize temporal information across modalities with different sampling rates.
Together, these advanced architectural innovations hint at the future of cross-sensory intelligence - AI systems that can perceive and process information in ways that more closely resemble human cognition, where our understanding of the world emerges from the integration of all our senses working in concert. This holistic processing allows for more robust comprehension that leverages complementary information across modalities, enables more natural human-AI interaction that doesn't require humans to adapt their communication style to the system, and supports more sophisticated reasoning about real-world situations that inherently involve multiple sensory dimensions.
5.3 Video and Cross-Modal Research Directions
So far, we've seen how multimodal models can read text, interpret images, and even listen to audio. But the real world unfolds in time. Understanding video means not just recognizing objects in frames, but reasoning about motion, causality, and events across time. This temporal dimension adds tremendous complexity because it requires models to track entities as they move, change, appear, and disappear throughout a sequence.
For instance, understanding a cooking video requires tracking ingredients as they transform through various stages of preparation. Similarly, comprehending sports footage demands following players as they move across a field and interact with each other and the ball. These capabilities go far beyond static image recognition, requiring the model to maintain a coherent understanding of objects' identities and relationships as they evolve through time.
At the same time, many real-world tasks require more than one sense at once. A teacher may speak while showing slides, or a person may gesture while giving instructions. This is where cross-modal reasoning becomes essential — connecting multiple streams of input into a unified interpretation. Humans naturally integrate information across senses, allowing us to connect a speaker's voice to their facial movements, associate sounds with their visual sources, and understand demonstrations that combine verbal explanations with visual examples.
Creating AI systems with this capability requires sophisticated architectures that can not only process each modality effectively but also align and integrate information across them. For example, when watching an instructional video, the system must synchronize the spoken narration with the corresponding visual demonstrations, understanding which words refer to which objects or actions on screen. This form of cross-modal grounding is fundamental to creating truly helpful assistants that can understand and engage with the world as humans do.
5.3.1 Video Understanding with Transformers
Unlike images, video is a sequence of frames — and sequences are exactly what transformers excel at. The challenge is that video sequences are often huge: a 10-second clip at 30 FPS has 300 frames. Feeding all of that directly into a transformer would be computationally impossible with current hardware limitations.
For context, many image-based transformers struggle with processing even a few high-resolution images simultaneously, so handling hundreds of sequential frames would require exponentially more memory and processing power. The computational complexity grows quadratically with sequence length due to the self-attention mechanism, where each token must attend to every other token. With video, this problem is magnified dramatically.
To illustrate the scale of this challenge: if a single high-resolution frame requires processing 1,024 tokens (a modest estimate), then a 10-second video at 30 FPS would need to process 307,200 tokens simultaneously. The self-attention computation for this would involve approximately 94 billion token-to-token comparisons. Even with modern GPUs and TPUs, this is prohibitively expensive in terms of both memory requirements and computational time.
This computational burden becomes even more significant when we consider real-world applications. For instance, video analysis for security surveillance might involve processing hours of footage, potentially generating millions of frames. Similarly, content moderation systems for social media platforms need to analyze thousands of videos uploaded every minute. The sheer volume of data makes naive approaches to video processing with transformers unfeasible.
The memory requirements also present a substantial barrier. Self-attention matrices grow quadratically with input length, so a video with twice as many frames requires four times the memory. Modern GPUs typically have 16-80GB of VRAM, which would be quickly exhausted by even modest-length videos processed at full resolution. This memory constraint has forced researchers to develop specialized architectures and optimization techniques specifically for video understanding.
Additionally, video data presents unique temporal dependencies that span across frames. While a transformer could theoretically capture these relationships, the sheer volume of cross-frame connections creates a computational bottleneck that requires innovative architectural solutions beyond simply scaling up existing image transformer models.
Techniques to Handle Video
Frame sampling
Select only key frames or use a sliding window. This approach reduces computational load by choosing representative frames at regular intervals (e.g., every 5th frame) or focusing on frames with significant visual changes. While this sacrifices some temporal detail, it captures the essential content while making processing feasible.
Frame sampling is particularly effective when videos contain redundant information across consecutive frames. For example, in a surveillance video where the scene remains mostly static, processing every frame would be wasteful. By intelligently selecting frames, models can maintain high accuracy while dramatically reducing processing requirements.
The selection process can employ various strategies beyond simple fixed-interval sampling. Adaptive sampling techniques can analyze motion vectors or pixel differences between frames to determine when important changes occur. This allows more frames to be sampled during high-action sequences and fewer during static scenes, optimizing the information-to-computation ratio.
Additionally, sliding window approaches maintain temporal continuity by processing overlapping sets of frames. Rather than treating each frame in isolation, these methods analyze short sequences (e.g., 8-16 frames) at a time, sliding the window forward to progress through the video. This preserves short-term temporal relationships while keeping computation manageable.
In more detail, frame sampling works by strategically selecting a subset of frames from the complete video sequence. There are several methods for this selection, each with its own advantages for different video analysis scenarios:
- Uniform sampling: Taking frames at fixed intervals (e.g., one frame per second) to provide an even representation across the entire video. This approach is computationally efficient and works well for videos with consistent action or gradual changes. Uniform sampling reduces the computational burden by processing only a fraction of the total frames while maintaining temporal coverage across the entire video duration.When implementing uniform sampling, researchers typically define a sampling rate based on factors like video length, content type, and available computational resources.
For instance, action-heavy videos might require higher sampling rates (e.g., 2-3 frames per second) to capture quick movements, while slow-changing scenes might need only one frame every few seconds.The main advantage of uniform sampling is its simplicity and predictability. Since frames are selected at regular intervals, the model receives a consistent temporal distribution that spans the entire video without bias toward any particular segment. This helps prevent overfitting to specific temporal regions and ensures the model learns patterns that generalize across the entire timeline.
For example, in a wildlife documentary tracking animal migration, capturing one frame every few seconds can adequately represent the overall movement patterns while significantly reducing processing requirements. This approach would effectively showcase the gradual progression of herds across landscapes without needing to process every minute detail of movement between consecutive frames. The sampling rate can be adjusted based on the speed of migration – faster movements might require more frequent sampling, while slower journeys could be represented with fewer frames.
- Content-aware sampling: Using algorithms to detect significant visual changes and selecting frames only when meaningful transitions occur. This is particularly useful for videos with static scenes interrupted by important events.These methods analyze frame-to-frame differences in features like color histograms, edge patterns, or motion vectors to identify when something interesting happens. In surveillance footage, for instance, this approach might capture frames only when a person enters the frame, ignoring long periods where nothing changes.Content-aware sampling works by establishing baseline metrics for the visual content, then continuously monitoring for deviations that exceed predefined thresholds.
For example, the system might calculate the pixel-wise difference between consecutive frames, the change in distribution of colors, or the emergence of new edge patterns that could indicate new objects.More sophisticated implementations use computer vision techniques such as object detection and tracking to identify semantically meaningful changes. Rather than just measuring raw pixel differences, these systems can recognize when a new person appears, when an object moves significantly, or when the overall scene composition changes.
The computational efficiency gained through content-aware sampling can be dramatic. In a typical 24-hour surveillance video where activity occurs for only 30 minutes total, this approach might reduce the processing load by 97%, while still capturing all relevant events. This makes real-time video analysis feasible even with limited computing resources.Beyond surveillance, content-aware sampling proves valuable in domains like autonomous driving (capturing frames when traffic conditions change), medical monitoring (detecting significant patient movements), and sports analytics (identifying key plays in lengthy game footage).
- Keyframe extraction: Identifying frames that contain the most representative or information-rich content, often based on visual features or scene boundaries. These algorithms use techniques like clustering, where frames are grouped based on visual similarity, and the most central frame from each cluster is selected. This approach effectively condenses videos into their essential visual components while discarding redundant or transitional frames.
The clustering process typically involves converting each frame into feature vectors using techniques like convolutional neural networks (CNNs), then applying algorithms such as k-means or hierarchical clustering to group similar frames. Once clusters are formed, the frame closest to each cluster's centroid is selected as the keyframe, providing a diverse yet comprehensive sampling of the video's visual content.
For example, in a 30-minute documentary, keyframe extraction might identify just 20-30 frames that collectively represent all the major scenes, locations, and subjects, drastically reducing the processing requirements while preserving the core visual narrative.
Advanced methods may incorporate semantic understanding to identify frames that best capture the narrative elements of a video, such as those showing critical actions in a sports highlight or key emotional moments in a movie scene. These approaches go beyond low-level visual features to consider higher-level concepts like object interactions, facial expressions, and scene composition.
Modern keyframe extraction systems often employ deep learning models trained to recognize important visual moments based on millions of human-annotated videos. This allows them to prioritize frames with storytelling significance rather than just visual distinctiveness. For instance, in an interview video, the system might select frames showing important gestures or facial reactions rather than visually different but narratively insignificant background changes.
Some systems also incorporate additional contextual cues like audio peaks, subtitle changes, or scene transitions to better identify moments of importance. This multimodal approach ensures that keyframes align with significant developments in the video's content rather than just visual variations.
The computational benefits are substantial. For example, processing just 10% of frames can reduce memory requirements by 90% and computation time by a similar amount. This makes previously impossible tasks manageable with current hardware.
However, there are tradeoffs to consider. Fast-moving objects might appear to "teleport" between sampled frames, and subtle movements might be missed entirely. Researchers mitigate these issues by combining frame sampling with optical flow estimation or interpolation techniques that can reconstruct information about the skipped frames.
Temporal embeddings
Add position encodings for time as well as space. This technique extends the transformer's position embeddings to include temporal information, allowing the model to understand both where objects are located within frames and how they move across frames. These encodings help the model distinguish between identical frames appearing at different points in a sequence.
Temporal embeddings are crucial for video understanding because they provide essential context about when events occur in a sequence. Just as spatial position embeddings help transformers understand the arrangement of elements within an image, temporal embeddings encode the chronological order and relative timing of frames. This temporal awareness is particularly important when analyzing activities that unfold over time, like a person throwing a ball or a car turning at an intersection.
Without such temporal context, a model would struggle to differentiate between similar-looking frames that appear at different times in a video. For instance, in a cooking video where ingredients are added to a pot multiple times, the model needs to understand the sequence of additions to correctly interpret the recipe steps. Temporal embeddings provide this critical ordering information.
These embeddings can be implemented in several ways. One approach uses sinusoidal functions similar to those in the original transformer architecture, but with an additional dimension for time. Another method employs learnable embeddings specifically trained to capture temporal relationships. Some advanced systems use a combination of absolute time position (the frame's position in the entire sequence) and relative timing information (how far apart frames are from each other).
The sinusoidal approach has the advantage of being able to generalize to sequence lengths not seen during training, while learnable embeddings often capture more nuanced temporal patterns but may struggle with very long sequences. Researchers often experiment with both approaches to find the optimal solution for specific video understanding tasks.
Some advanced implementations also incorporate temporal embeddings at multiple scales. For instance, they might encode information about a frame's position within a second, a minute, and the entire video. This multi-scale approach helps models understand both fine-grained actions and longer narrative arcs within videos.
For example, in a video of a basketball game, temporal embeddings would help the model recognize that a player jumping, then releasing a ball, followed by the ball moving through a hoop represents a shooting sequence. Without temporal embeddings, these frames might be interpreted as disconnected events rather than a coherent action. The embeddings provide the critical temporal context that links these frames into a meaningful sequence.
Similarly, in a surveillance video, temporal embeddings allow the model to track individuals across frames and understand the progression of activities. This capability is essential for applications like activity recognition, where the order of actions defines the activity (e.g., entering a building versus leaving it involves the same frames in reverse order).
Hierarchical modeling
First process frames locally, then reason globally across segments. This multi-level approach initially treats smaller chunks of consecutive frames as units for local processing, extracting features about motion and changes. This hierarchical structure mirrors how humans understand videos - we first comprehend small actions and then connect them into larger narratives. The hierarchical approach is inspired by cognitive science research showing that human perception operates at multiple temporal scales simultaneously, from millisecond reactions to minute-long comprehension of complex scenes.
At the local level, models typically process 8-16 consecutive frames using lightweight attention mechanisms. This allows the model to capture short-term dynamics like object movement, facial expressions, or scene transitions without requiring extensive computational resources. These local processors extract rich representations that summarize what's happening in each small segment of video. The temporal receptive field at this level is carefully balanced - too few frames would miss important motion patterns, while too many would increase computational burden exponentially. Research has shown that 8-16 frames typically provides sufficient context to identify atomic actions while remaining computationally feasible.
These local processors employ specialized architectures like factorized attention or 3D convolutions that efficiently model spatiotemporal relationships. Some implementations use causal masking to ensure the model only attends to current and past frames, enabling real-time processing for applications like autonomous driving or security monitoring. Others process bidirectionally to maximize information extraction for offline analysis.
Then, a higher-level transformer processes these compressed representations to understand longer-term patterns and relationships across the entire video, effectively compressing the temporal dimension while preserving critical information. This global processor receives the local features as input and applies attention across them, enabling the model to recognize complex patterns like cause-effect relationships, recurring motifs, or narrative arcs that span minutes rather than seconds. The global transformer's design often includes specialized mechanisms for handling temporal distance, such as relative position encodings that help the model understand how far apart events occur in time.
This multi-resolution approach also addresses the challenge of variable information density in videos. Action-packed segments might require more detailed analysis, while static scenes need less processing. Advanced implementations dynamically allocate computational resources based on content complexity, spending more computation on informative segments.
For example, in a cooking video, local processing might identify individual actions like "chopping vegetables" or "stirring pot," while global processing would connect these into the complete recipe sequence and understand the relationship between early preparation steps and the final dish. This two-tier approach dramatically reduces computational complexity compared to processing all frames simultaneously while maintaining the ability to capture both fine-grained motions and long-range dependencies. In practical terms, this hierarchical design can reduce memory requirements by 80-90% compared to flat attention across all frames, making it possible to analyze longer videos on standard hardware.
5.3.2 VideoGPT
VideoGPT is a generative model for video built on the transformer architecture, advancing beyond static image generation by incorporating temporal aspects. This model adapts the powerful capabilities of transformer-based language models to the video domain, enabling it to understand and generate complex visual sequences over time. By extending the core principles of text generation to video, VideoGPT demonstrates how the transformer paradigm can be effectively applied across different modalities.
VideoGPT treats video as a sequence of image tokens, converting each frame into a discrete representation that can be processed sequentially. This tokenization process typically involves using a VQ-VAE (Vector Quantized Variational Autoencoder) to compress video frames into a more manageable representation. The resulting tokens form a vocabulary of visual elements that the model can manipulate, similar to how language models work with word tokens. This compression step is crucial because it reduces the dimensionality of the raw video data from millions of pixel values to thousands of discrete tokens, making the subsequent modeling task computationally feasible while preserving essential visual information.
The tokenization process works by training the VQ-VAE to encode frames into a latent space and then quantizing these continuous representations into a finite set of discrete codes from a learned codebook. This codebook effectively becomes the "visual vocabulary" of the model. The transformer component of VideoGPT then learns to predict the next token in this sequence, capturing both short-term frame-to-frame transitions and longer-term visual narratives.
The model learns both spatial and temporal dynamics, enabling frame prediction and video synthesis. This dual capability allows VideoGPT to understand not just what objects appear in a scene (spatial understanding), but how they move and interact over time (temporal understanding). The spatial component handles the composition within individual frames—elements like object appearance, lighting, and scene layout. The temporal component captures motion patterns, object persistence, and causal relationships between events across frames.
This architecture employs masked modeling techniques similar to those used in language models, where certain tokens are hidden during training and the model must predict them. In VideoGPT's case, this might involve masking future frames and training the model to predict them based on past frames, essentially learning to forecast how scenes will evolve.
For example, it can predict how a bouncing ball will continue its trajectory or how water will flow in a stream. This makes VideoGPT useful for applications like video completion, prediction of future frames, and even fully generative video synthesis from text prompts. Beyond these creative applications, the model's understanding of visual dynamics can benefit fields like robotics (predicting physical interactions), autonomous driving (anticipating traffic movements), and virtual reality (generating realistic environmental responses).
Example: Implementing VideoGPT: Video Generation with Transformers
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import numpy as np
import os
from tqdm import tqdm
# 1. VQ-VAE for Video Frame Tokenization
class VQVAE(nn.Module):
def __init__(self, input_dim=3, hidden_dim=128, num_embeddings=1024, embedding_dim=64):
super().__init__()
# Encoder: Convert raw frames to continuous latent space
self.encoder = nn.Sequential(
nn.Conv2d(input_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_dim, embedding_dim, kernel_size=3, stride=1, padding=1)
)
# Vector Quantization: Map continuous vectors to discrete codebook entries
self.codebook = nn.Embedding(num_embeddings, embedding_dim)
self.codebook.weight.data.uniform_(-1.0 / num_embeddings, 1.0 / num_embeddings)
# Decoder: Reconstruct frames from quantized tokens
self.decoder = nn.Sequential(
nn.Conv2d(embedding_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(hidden_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(hidden_dim, input_dim, kernel_size=4, stride=2, padding=1),
nn.Tanh()
)
def encode(self, x):
z = self.encoder(x)
return z
def quantize(self, z):
# Reshape z for quantization
z_flattened = z.permute(0, 2, 3, 1).contiguous().view(-1, z.shape[1])
# Calculate distances to codebook vectors
d = torch.sum(z_flattened**2, dim=1, keepdim=True) + \
torch.sum(self.codebook.weight**2, dim=1) - \
2 * torch.matmul(z_flattened, self.codebook.weight.t())
# Find nearest codebook vector
min_encoding_indices = torch.argmin(d, dim=1)
z_q = self.codebook(min_encoding_indices).view(z.shape[0], z.shape[2], z.shape[3], z.shape[1])
z_q = z_q.permute(0, 3, 1, 2).contiguous()
# Straight-through estimator for gradients
z_q_sg = z + (z_q - z).detach()
return z_q_sg, min_encoding_indices.view(z.shape[0], z.shape[2], z.shape[3])
def decode(self, z_q):
return self.decoder(z_q)
def forward(self, x):
z = self.encode(x)
z_q_sg, indices = self.quantize(z)
x_recon = self.decode(z_q_sg)
return x_recon, z, z_q_sg, indices
# 2. Transformer for Video Prediction
class VideoGPTTransformer(nn.Module):
def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6,
dim_feedforward=2048, max_seq_length=256):
super().__init__()
self.d_model = d_model
# Token embedding: Convert discrete tokens to continuous vectors
self.token_embedding = nn.Embedding(vocab_size, d_model)
# Position encoding: Add information about token position in sequence
self.pos_encoder = nn.Parameter(torch.zeros(1, max_seq_length, d_model))
# Transformer encoder layers
encoder_layers = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True
)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
# Output head: Project to token probabilities
self.output_head = nn.Linear(d_model, vocab_size)
def forward(self, src, src_mask=None):
# src shape: [batch_size, seq_len]
batch_size, seq_len = src.shape
# Embed tokens and add positional encoding
src = self.token_embedding(src) * np.sqrt(self.d_model)
src = src + self.pos_encoder[:, :seq_len, :]
# Pass through transformer
output = self.transformer_encoder(src, src_mask)
# Project to vocabulary space
output = self.output_head(output)
return output
# 3. Dataset for processing video frames
class VideoDataset(Dataset):
def __init__(self, video_dir, frame_size=(64, 64), frames_per_clip=16, transform=None):
self.video_paths = [os.path.join(video_dir, f) for f in os.listdir(video_dir)
if f.endswith(('.mp4', '.avi'))]
self.frame_size = frame_size
self.frames_per_clip = frames_per_clip
self.transform = transform or transforms.Compose([
transforms.Resize(frame_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
def __len__(self):
return len(self.video_paths)
def __getitem__(self, idx):
import cv2
video_path = self.video_paths[idx]
cap = cv2.VideoCapture(video_path)
# Calculate frame sampling
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_indices = np.linspace(0, total_frames-1, self.frames_per_clip, dtype=int)
# Extract frames
frames = []
for frame_idx in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
ret, frame = cap.read()
if ret:
# Convert BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Apply transforms
if self.transform:
frame = self.transform(frame)
frames.append(frame)
cap.release()
# Stack frames along a new dimension
return torch.stack(frames) # Shape: [frames_per_clip, channels, height, width]
# 4. Training Functions
def train_vqvae(vqvae, dataloader, optimizer, epochs=10, device='cuda'):
vqvae.to(device)
for epoch in range(epochs):
total_loss = 0
for batch_idx, frames in enumerate(tqdm(dataloader)):
frames = frames.to(device) # [B, T, C, H, W]
batch_size, time_steps = frames.shape[:2]
# Reshape to process all frames at once
frames_flat = frames.view(-1, *frames.shape[2:]) # [B*T, C, H, W]
optimizer.zero_grad()
# Forward pass through VQ-VAE
x_recon, z, z_q, indices = vqvae(frames_flat)
# Calculate losses
recon_loss = F.mse_loss(x_recon, frames_flat)
# VQ-VAE commitment loss
vq_loss = F.mse_loss(z_q, z.detach())
commitment_loss = F.mse_loss(z, z_q.detach())
# Combined loss
loss = recon_loss + vq_loss + 0.25 * commitment_loss
# Backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
return vqvae
def train_transformer(transformer, vqvae, dataloader, optimizer, epochs=10, device='cuda'):
transformer.to(device)
vqvae.to(device).eval()
for epoch in range(epochs):
total_loss = 0
for batch_idx, frames in enumerate(tqdm(dataloader)):
frames = frames.to(device) # [B, T, C, H, W]
batch_size, time_steps = frames.shape[:2]
# Reshape to process all frames at once
frames_flat = frames.view(-1, *frames.shape[2:]) # [B*T, C, H, W]
# Get token indices from VQ-VAE
with torch.no_grad():
z = vqvae.encode(frames_flat)
_, indices = vqvae.quantize(z)
# Reshape indices back to [batch_size, time_steps, height, width]
indices = indices.view(batch_size, time_steps, *indices.shape[1:])
# Flatten spatial dimensions to get sequence of tokens per frame
# [batch_size, time_steps, height*width]
token_sequences = indices.reshape(batch_size, time_steps, -1)
# For transformer training, we predict next tokens
src = token_sequences[:, :-1].reshape(batch_size, -1) # Input sequence
tgt = token_sequences[:, 1:].reshape(batch_size, -1) # Target sequence
optimizer.zero_grad()
# Create attention mask (optional for training efficiency)
seq_len = src.shape[1]
attn_mask = torch.triu(torch.ones(seq_len, seq_len) * float('-inf'), diagonal=1).to(device)
# Forward pass
output = transformer(src, attn_mask)
# Calculate loss
loss = F.cross_entropy(output.reshape(-1, output.size(-1)), tgt.reshape(-1))
# Backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
return transformer
# 5. Main: Putting it all together
def main():
# Hyperparameters
batch_size = 8
frames_per_clip = 16
frame_size = (64, 64)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Create dataset and dataloader
dataset = VideoDataset(
video_dir="path/to/videos",
frame_size=frame_size,
frames_per_clip=frames_per_clip
)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)
# Step 1: Train VQ-VAE
vqvae = VQVAE(input_dim=3, hidden_dim=128, num_embeddings=1024, embedding_dim=64)
vqvae_optimizer = torch.optim.Adam(vqvae.parameters(), lr=3e-4)
vqvae = train_vqvae(vqvae, dataloader, vqvae_optimizer, epochs=10, device=device)
# Save VQ-VAE model
torch.save(vqvae.state_dict(), "vqvae_model.pth")
# Step 2: Train Transformer
# Number of tokens = codebook size (from VQ-VAE)
vocab_size = 1024 + 1 # +1 for padding token
transformer = VideoGPTTransformer(vocab_size=vocab_size, d_model=512, nhead=8, num_layers=6)
transformer_optimizer = torch.optim.Adam(transformer.parameters(), lr=1e-4)
transformer = train_transformer(transformer, vqvae, dataloader, transformer_optimizer, epochs=20, device=device)
# Save Transformer model
torch.save(transformer.state_dict(), "transformer_model.pth")
# 6. Video Generation Function
def generate_video(vqvae, transformer, seed_frames, num_frames_to_generate=16, device='cuda'):
vqvae.to(device).eval()
transformer.to(device).eval()
# Process seed frames through VQ-VAE to get tokens
with torch.no_grad():
seed_frames = seed_frames.to(device)
z = vqvae.encode(seed_frames)
_, indices = vqvae.quantize(z)
# Flatten spatial dimensions to get sequence of tokens
token_sequence = indices.reshape(1, -1) # [1, time*height*width]
# Generate new frames one by one
generated_tokens = token_sequence.clone()
for _ in range(num_frames_to_generate):
# Predict next tokens
with torch.no_grad():
output = transformer(generated_tokens)
next_token_logits = output[:, -1, :]
next_tokens = torch.argmax(next_token_logits, dim=-1, keepdim=True)
generated_tokens = torch.cat([generated_tokens, next_tokens], dim=1)
# Extract only the newly generated tokens
new_tokens = generated_tokens[:, token_sequence.shape[1]:]
# Reshape tokens to match expected input for VQ-VAE decoder
h, w = indices.shape[1], indices.shape[2]
new_tokens = new_tokens.reshape(-1, h, w) # [num_frames_to_generate, height, width]
# Decode tokens to frames
generated_frames = []
with torch.no_grad():
for tokens in new_tokens:
tokens = tokens.unsqueeze(0) # Add batch dimension
z_q = vqvae.codebook(tokens.view(-1)).view(1, -1, vqvae.codebook.embedding_dim)
z_q = z_q.permute(0, 2, 1).view(1, vqvae.codebook.embedding_dim, h, w)
frame = vqvae.decode(z_q)
generated_frames.append(frame)
# Stack frames along time dimension
return torch.cat(generated_frames, dim=0) # [num_frames_to_generate, C, H, W]
if __name__ == "__main__":
main()
Detailed Explanation of VideoGPT Implementation
This example demonstrates a comprehensive approach to video generation using the VideoGPT architecture. Let's break down the key components:
1. Vector Quantized Variational Autoencoder (VQ-VAE)
The VQ-VAE forms the foundation of VideoGPT by converting raw video frames into discrete tokens:
- Encoder: Compresses video frames into a lower-dimensional continuous latent space using convolutional layers.
- Vector Quantization: Maps these continuous vectors to the nearest vectors in a learned "codebook," effectively discretizing the representation.
- Decoder: Reconstructs the original frames from the quantized representations.
- Straight-through estimator: A technique used during training to allow gradients to flow through the non-differentiable quantization step.
This tokenization process is crucial because it reduces the dimensionality of video data from millions of pixel values to a more manageable set of discrete tokens, making the subsequent modeling task computationally feasible.
2. Transformer Architecture
Once the video frames are tokenized, a transformer model predicts the next tokens in sequence:
- Token Embedding: Converts discrete tokens into continuous vector representations.
- Positional Encoding: Adds information about each token's position in the sequence.
- Transformer Encoder: Processes the token embeddings using self-attention mechanisms to capture dependencies between tokens.
- Output Head: Projects the transformer's output back to token probabilities for prediction.
The transformer architecture allows the model to understand complex spatial-temporal patterns within videos, capturing both short-term frame-to-frame transitions and longer-term visual narratives.
3. Dataset Handling
The custom VideoDataset class handles video processing:
- Extracts frames from video files at regular intervals.
- Applies transformations (resize, normalize) to prepare frames for the model.
- Packages frames into clips of a specified length.
4. Training Process
The training happens in two distinct phases:
- VQ-VAE Training: Optimizes the encoder, codebook, and decoder to effectively compress and reconstruct video frames while building a meaningful discrete representation.
- Transformer Training: After the VQ-VAE is trained, video frames are tokenized and fed to the transformer, which learns to predict future tokens based on past ones.
5. Video Generation
The generation process reverses the training pipeline:
- Seed frames are tokenized through the VQ-VAE encoder and quantizer.
- The transformer autoregressively generates new tokens one by one.
- These tokens are then decoded back into video frames using the VQ-VAE decoder.
Key Technical Insights
- Two-stage Architecture: Separating representation learning (VQ-VAE) from sequence modeling (transformer) makes training more stable and efficient.
- Spatial-Temporal Modeling: The model must capture both spatial relationships within frames and temporal dependencies across frames.
- Autoregressive Generation: Videos are generated one token at a time, with each new token conditioned on all previous tokens.
- Computational Efficiency: Working with discrete tokens rather than raw pixels drastically reduces the computational requirements.
This implementation demonstrates how transformer architectures, originally designed for language modeling, can be effectively adapted to video generation by incorporating appropriate tokenization strategies and handling the additional complexity of temporal data.
5.3.3 Gemini (DeepMind)
Gemini (DeepMind) is a sophisticated multimodal model that seamlessly integrates text, vision, and in some cases video within a unified architecture. Unlike earlier models that treated different data types in isolation, Gemini processes and reasons across multiple input formats simultaneously. This represents a significant advancement over previous approaches where text, images, and video were often processed by separate specialized models and then combined afterward. This unified approach allows Gemini to understand the contextual relationships between different modalities from the ground up rather than trying to merge separately processed information.
The model employs advanced cross-attention mechanisms that enable it to scale effectively across modalities. These attention mechanisms allow the model to identify relationships between elements in different formats—for example, connecting a textual description to relevant parts of an image or linking dialogue to visual events in a video sequence. This architecture enables information to flow bidirectionally between modalities, creating a more holistic understanding. Unlike simple concatenation of different input embeddings, Gemini's cross-attention system allows for dynamic weighting of information across modalities based on context and relevance, similar to how humans naturally shift focus between what they see and hear. This dynamic attention system helps the model determine which aspects of an image might be most relevant to a textual query, or conversely, which parts of a text prompt should inform the understanding of visual content.
Gemini demonstrates impressive reasoning capabilities across a wide range of multimodal inputs, including complex diagrams (such as scientific illustrations or technical schematics), video content (with temporal understanding), and multifaceted prompts that combine several input types. The model can process images at high resolution, enabling it to recognize fine details in photographs, charts, and documents.
For video analysis, Gemini can track objects over time, understand narrative progression, and even anticipate likely future developments based on visual dynamics. This capability is particularly valuable in scenarios requiring detailed visual analysis, such as interpreting medical imagery, understanding engineering diagrams, or analyzing sports footage to extract tactical insights.
This reasoning extends beyond simple recognition to include causal understanding, spatial relationships, and temporal sequences—allowing the model to answer questions like "What will happen next in this physical system?" or "How does this mechanism work?" while referencing visual material. The model's temporal understanding is crucial for tasks that involve processes unfolding over time, such as explaining chemical reactions, analyzing mechanical systems, or tracking changes in biological specimens. This capability resembles human experts' ability to "read" dynamic systems from static diagrams or limited video inputs.
Gemini's multimodal capabilities enable it to solve complex tasks requiring synthesis across modalities, such as interpreting a graph while considering textual context, explaining the steps of a visual process, or identifying inconsistencies between spoken narration and visual content. This integrated approach mirrors human cognition more closely than previous AI systems, as it can form connections between concepts across different representational formats.
This integration facilitates more natural human-AI interaction, allowing users to communicate with the system using whatever combination of text, images, or video best suits their needs, rather than being constrained to a single modality. For example, a user could ask Gemini to analyze a chart, compare it with historical data mentioned in an accompanying text, and explain apparent discrepancies—a task that requires seamless integration of visual and textual information.
Example: Gemini Implementation
import google.generativeai as genai
import PIL.Image
import os
from IPython.display import display, HTML
# Configure API key
genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
# List available models
for m in genai.list_models():
if 'generateContent' in m.supported_generation_methods:
print(m.name)
# Select Gemini Pro Vision model
model = genai.GenerativeModel('gemini-pro-vision')
# Function to analyze an image with text prompt
def analyze_image(image_path, prompt):
img = PIL.Image.open(image_path)
response = model.generate_content([prompt, img])
return response.text
# Function for multimodal reasoning with multiple images
def compare_images(image_path1, image_path2, prompt):
img1 = PIL.Image.open(image_path1)
img2 = PIL.Image.open(image_path2)
response = model.generate_content([prompt, img1, img2])
return response.text
# Example usage: Image analysis
image_analysis = analyze_image("chart.jpg",
"Analyze this chart in detail. What trends do you observe?")
print(image_analysis)
# Example usage: Image comparison
comparison = compare_images("design_v1.jpg", "design_v2.jpg",
"Compare these two design versions and explain the key differences.")
print(comparison)
# Example: Complex reasoning with image and specific instructions
reasoning = analyze_image("scientific_diagram.jpg",
"Explain how this biological process works. Focus on:
1. The starting materials
2. The transformation steps
3. The end products
4. The energy changes involved")
print(reasoning)
# Example: Video frame analysis
def analyze_video_frames(frame_paths, prompt):
frames = [PIL.Image.open(path) for path in frame_paths]
response = model.generate_content([prompt] + frames)
return response.text
frame_paths = ["video_frame1.jpg", "video_frame2.jpg", "video_frame3.jpg"]
video_analysis = analyze_video_frames(frame_paths,
"Analyze the motion sequence shown in these frames. What's happening?")
print(video_analysis)
# Safety settings example (optional)
safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_ONLY_HIGH"
}
]
model_with_safety = genai.GenerativeModel(
model_name='gemini-pro-vision',
safety_settings=safety_settings
)
Understanding the Gemini Implementation
The code example above demonstrates how to work with Google's Gemini multimodal model, providing a practical framework for integrating vision and language understanding. Let's explore the key components and capabilities:
API Configuration and Model Selection
The implementation begins by importing the necessary libraries and configuring the API with an authentication key. The code then lists available models with content generation capabilities before selecting the Gemini Pro Vision model, which is specifically designed for multimodal tasks combining text and images.
Core Functionality
The implementation provides several functions that showcase Gemini's multimodal capabilities:
- Single Image Analysis: The
analyze_image()function accepts an image path and a text prompt, then returns Gemini's interpretation of the image in the context of the prompt. This enables tasks like chart analysis, object identification, or scene description. - Comparative Image Analysis: With
compare_images(), the model can reason about relationships between multiple images, identifying similarities, differences, and patterns across visual content. This is useful for before/after comparisons, design iterations, or tracking changes. - Video Frame Analysis: Though Gemini doesn't process video directly in this implementation, the
analyze_video_frames()function demonstrates how to analyze temporal sequences by feeding multiple frames with a contextual prompt. This allows for basic motion analysis and event understanding across time.
Prompt Engineering for Multimodal Tasks
The example showcases several prompt structures that enable different types of visual reasoning:
- Open-ended analysis: "Analyze this chart in detail. What trends do you observe?" allows the model to identify and describe patterns with minimal constraints.
- Comparative analysis: "Compare these two design versions and explain the key differences" directs the model to focus specifically on contrasting visual elements.
- Structured reasoning: The scientific diagram prompt uses a numbered list to guide the model through a systematic analysis process, ensuring comprehensive coverage of specific aspects.
- Temporal understanding: "Analyze the motion sequence shown in these frames" encourages the model to consider relationships between images as representing a continuous process rather than isolated visuals.
Safety Considerations
The implementation includes optional safety settings that can be configured to control the model's outputs according to different harm categories and thresholds. This demonstrates how to implement responsible AI practices when deploying multimodal systems that might encounter or generate sensitive content.
Technical Significance
What makes this implementation particularly powerful is its simplicity relative to the complexity of the underlying model. The Gemini architecture internally handles the complex cross-attention mechanisms that align visual and textual information, allowing developers to interact with it through a straightforward API.
Unlike previous approaches that required separate models for vision and language tasks, Gemini's unified architecture enables it to process both modalities jointly, capturing the interactions between them. This is evident in how a single function call can pass both text and images to the model and receive coherent, contextually relevant responses.
Practical Applications
This implementation enables numerous real-world applications:
- Data visualization interpretation: Automatically generating insights from charts, graphs, and other visual data representations.
- Document understanding: Analyzing documents that combine text and images, such as technical manuals, academic papers, or illustrated guides.
- Educational content analysis: Processing instructional materials that use diagrams and text explanations to convey complex concepts.
- Design feedback: Providing structured analysis of visual designs, identifying issues and suggesting improvements.
- Medical image preliminary assessment: Assisting healthcare professionals by providing initial observations on medical imagery alongside clinical notes.
The example demonstrates how Gemini bridges the gap between computer vision and natural language processing, offering an integrated approach to understanding the visual world through the lens of language and vice versa.
5.3.4 Kosmos-2 (Microsoft)
Focuses on grounding language in vision, which means creating explicit connections between language descriptions and specific visual elements. This technique enables the model to understand not just what objects are in an image, but precisely where they are located and how they relate to linguistic references. The model essentially creates a detailed spatial map of the image, connecting language tokens directly to pixel regions. This grounding capability represents a fundamental shift in how AI processes visual information, moving from general scene understanding to precise object localization and reference resolution—similar to how humans point at objects while describing them. Just as a person might say "look at that red bird on the branch" while pointing, Kosmos-2 can conceptually "point" to objects it describes.
Can link words to objects in an image or video frame, enabling tasks like "point to the cat in the video." This capability represents a significant advancement over earlier models that could only describe images generally but couldn't identify specific regions or elements when prompted. For example, when asked "What is the person on the left wearing?", Kosmos-2 can both understand the spatial reference ("on the left") and ground its response to the specific person being referenced. It can generate bounding boxes or segmentation masks that highlight exactly which pixels in the image correspond to "the person on the left" before answering about their clothing. This requires sophisticated visual reasoning that combines object detection, spatial awareness, and natural language understanding in a unified framework—a computational challenge that previous generations of models struggled to address. The model must simultaneously parse language, recognize objects, understand spatial relationships, and maintain the connections between them all.
A step toward cross-modal grounding, where models tie abstract descriptions to concrete visual elements. This connection between language and vision mimics how humans naturally communicate about visual information, allowing for more precise visual reasoning, improved human-AI interaction, and the foundation for embodied AI systems that need to understand references to objects in their environment. Rather than treating language and vision as separate domains that occasionally interact, Kosmos-2 builds a shared representational space where concepts from either modality can be mapped directly to each other. The grounding capability is especially valuable for applications like visual question answering, image editing based on natural language instructions, and assistive technologies for the visually impaired. For instance, a visually impaired user could ask "Is there a cup on the table?" and receive not just a yes/no answer, but information about where exactly the cup is located relative to other objects.
By establishing direct links between words and visual regions, Kosmos-2 creates a foundation for more sophisticated reasoning tasks that require understanding both the semantics of language and the spatial configuration of visual scenes—capabilities that are essential for robots navigating physical environments, AR/VR systems responding to natural language commands about visible objects, or accessibility tools that help visually impaired users understand their surroundings through verbal descriptions. This grounding mechanism also enables multi-turn interactions about specific parts of an image, where a user might ask "What's in the corner?" followed by "What color is it?" and the model correctly maintains context about which object is being discussed. The alignment between language and vision provides a crucial building block for AI systems that must operate in the physical world, where understanding references to objects and their relationships is fundamental to meaningful interaction.
Example: Kosmos-2 Implementation
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import requests
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# Load Kosmos-2 model and processor
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
# Function to get image from URL
def get_image(url):
image = Image.open(requests.get(url, stream=True).raw)
return image
# Function to process image and generate caption with bounding boxes
def analyze_with_grounding(image, prompt="<grounding>Describe this image in detail:"):
# Process the image and text
inputs = processor(text=prompt, images=image, return_tensors="pt")
# Generate output from model
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
num_beams=5,
early_stopping=True
)
# Decode the generated text with grounding tokens
generated_text = processor.batch_decode(outputs, skip_special_tokens=False)[0]
# Extract bounding boxes from the generated output
phrase_bboxes = []
for phrase, bbox in processor.parse_bbox(generated_text):
if bbox is not None:
phrase_bboxes.append((phrase, bbox))
return generated_text, phrase_bboxes
# Function to visualize the image with bounding boxes
def visualize_with_bboxes(image, phrase_bboxes):
plt.figure(figsize=(16, 10))
plt.imshow(image)
ax = plt.gca()
# Add bounding boxes with labels
for phrase, bbox in phrase_bboxes:
x, y, width, height = bbox
rect = patches.Rectangle(
(x * image.width, y * image.height),
width * image.width,
height * image.height,
linewidth=2,
edgecolor='r',
facecolor='none'
)
ax.add_patch(rect)
plt.text(
x * image.width,
y * image.height - 5,
phrase,
color='white',
backgroundcolor='red',
fontsize=10
)
plt.axis('off')
plt.tight_layout()
plt.show()
# Function for comparing objects in an image
def compare_objects(image, prompt="<grounding>Compare the objects in this image:"):
generated_text, phrase_bboxes = analyze_with_grounding(image, prompt)
print("Generated Text:", generated_text)
visualize_with_bboxes(image, phrase_bboxes)
return generated_text, phrase_bboxes
# Function for referring expression comprehension
def find_specific_object(image, object_description):
prompt = f"<grounding>Point to the {object_description} in this image."
generated_text, phrase_bboxes = analyze_with_grounding(image, prompt)
print(f"Looking for: {object_description}")
print("Generated Text:", generated_text)
visualize_with_bboxes(image, phrase_bboxes)
return generated_text, phrase_bboxes
# Example usage
image_url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/living_room.jpg"
image = get_image(image_url)
# Basic image description with grounding
description, bboxes = analyze_with_grounding(image)
print("Description with grounding:")
print(description)
visualize_with_bboxes(image, bboxes)
# Find a specific object
find_specific_object(image, "red couch")
# Compare objects
compare_objects(image, "<grounding>Compare the furniture items in this image.")
# Spatial reasoning example
spatial_reasoning = find_specific_object(image, "lamp next to the couch")
Understanding the Kosmos-2 Implementation
The code example above demonstrates how to work with Microsoft's Kosmos-2 multimodal model, showcasing its unique capability for visual grounding. Let's break down the key components and capabilities:
Setup and Initialization
The implementation begins by importing the necessary libraries and initializing the Kosmos-2 model and processor from the Hugging Face Transformers library. Kosmos-2 is accessed through the AutoModelForVision2Seq class, which handles models that can process both vision and language.
Core Grounding Functionality
The central function analyze_with_grounding() demonstrates Kosmos-2's key innovation: the ability to connect language descriptions with specific visual elements through grounding. The function:
Processes an image along with a prompt that includes the special `` token to activate the model's grounding capabilities
- Generates a descriptive response about the image
- Extracts bounding box coordinates for objects that the model has identified and mentioned
- Returns both the generated text and a list of phrase-bounding box pairs
Visual Grounding in Action
The visualize_with_bboxes() function provides a visualization capability that overlays the model's detected objects on the original image. This visual representation shows how Kosmos-2 connects its language understanding with precise spatial locations in the image, effectively demonstrating the model's ability to "point" at objects it's describing.
Advanced Visual Reasoning Capabilities
The implementation includes specialized functions that showcase different aspects of Kosmos-2's visual reasoning abilities:
- Object Comparison: The
compare_objects()function prompts the model to identify and compare multiple objects in an image, highlighting each with bounding boxes. This demonstrates the model's ability to reason about relationships between different visual elements. - Referring Expression Comprehension: With
find_specific_object(), the model locates specific objects based on natural language descriptions. This capability is essential for tasks requiring precise object localization based on verbal instructions. - Spatial Reasoning: The example shows how Kosmos-2 can understand spatial relationships between objects (e.g., "lamp next to the couch"), combining object recognition with positional awareness.
Prompt Engineering for Grounding
The example highlights the importance of the `` token in prompts, which serves as a special instruction to the model to activate its visual grounding capabilities. Different prompting strategies demonstrate various aspects of visual reasoning:
- "Describe this image in detail" triggers comprehensive scene understanding with object localization
- "Point to the [object]" focuses the model on locating a specific item
- "Compare the objects" encourages the model to identify multiple entities and reason about their similarities and differences
Technical Significance
What makes Kosmos-2 particularly innovative is its ability to create explicit connections between natural language descriptions and specific regions in an image. Unlike earlier multimodal models that could generally describe an image but couldn't pinpoint specific objects, Kosmos-2's grounding mechanism enables:
- Precise object localization in response to natural language queries
- Fine-grained understanding of spatial relationships between objects
- The ability to answer questions about specific parts of an image
- More natural human-AI interaction by mimicking how humans point while describing
Practical Applications
This implementation of Kosmos-2 enables numerous real-world applications:
- Assistive technology: Helping visually impaired users understand their surroundings by describing specific objects and their locations
- Visual search: Finding objects in images based on natural language descriptions
- Human-robot interaction: Enabling robots to understand references to objects in their environment
- Visual question answering: Providing detailed answers about specific elements in an image
- Educational tools: Creating interactive learning experiences that connect visual concepts with language
Kosmos-2 represents an important step toward AI systems that can perceive and reason about the visual world in ways that more closely resemble human understanding, bridging the gap between seeing and communicating about what is seen.
Example: Extracting Features from Video with Hugging Face
We can’t yet run full Gemini or Kosmos in open-source, but we can use pretrained models like VideoMAE for video embeddings.
from transformers import VideoMAEFeatureExtractor, VideoMAEModel
import torch
import av # pip install av
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os
import time
def load_video_mae_model():
"""Load the pretrained VideoMAE model and feature extractor"""
print("Loading VideoMAE model...")
feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
return feature_extractor, model
def extract_frames(video_path, num_frames=8, sample_rate=30):
"""Extract frames from a video file at a specific sample rate
Args:
video_path: Path to the video file
num_frames: Maximum number of frames to extract
sample_rate: Extract every nth frame
Returns:
List of frames as numpy arrays in RGB format
"""
print(f"Extracting frames from {video_path}...")
if not os.path.exists(video_path):
raise FileNotFoundError(f"Video file not found: {video_path}")
container = av.open(video_path)
frames = []
# Get video info
video_stream = container.streams.video[0]
fps = video_stream.average_rate
duration = container.duration / 1000000 # in seconds
total_frames = video_stream.frames
print(f"Video info: {fps} fps, {duration:.2f}s duration, {total_frames} total frames")
start_time = time.time()
for i, frame in enumerate(container.decode(video=0)):
if i % sample_rate == 0:
frames.append(frame.to_ndarray(format="rgb24"))
print(f"Extracted frame {len(frames)}/{num_frames} (video position: {i})")
if len(frames) == num_frames:
break
process_time = time.time() - start_time
print(f"Frame extraction complete. Extracted {len(frames)} frames in {process_time:.2f}s")
return frames
def get_video_embeddings(feature_extractor, model, frames):
"""Process frames and extract embeddings using VideoMAE
Args:
feature_extractor: VideoMAE feature extractor
model: VideoMAE model
frames: List of video frames as numpy arrays
Returns:
Video embeddings tensor and raw model outputs
"""
if len(frames) == 0:
raise ValueError("No frames were extracted from the video")
print(f"Processing {len(frames)} frames with VideoMAE...")
# Preprocess frames
inputs = feature_extractor(frames, return_tensors="pt")
# Extract embeddings
with torch.no_grad():
outputs = model(**inputs)
video_embeddings = outputs.last_hidden_state
return video_embeddings, outputs
def visualize_frames_and_embeddings(frames, embeddings):
"""Visualize extracted frames and a 2D PCA projection of their embeddings"""
# Visualize frames
num_frames = len(frames)
fig, axes = plt.subplots(1, num_frames, figsize=(16, 4))
for i, (frame, ax) in enumerate(zip(frames, axes)):
ax.imshow(frame)
ax.set_title(f"Frame {i}")
ax.axis('off')
plt.tight_layout()
plt.savefig("video_frames.png")
plt.show()
# Visualize embedding patterns (simple 2D visualization)
# Take mean over tokens dimension to get per-frame representations
frame_embeddings = embeddings.mean(dim=1).squeeze(0)
# PCA-like dimensionality reduction (simplified)
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
reduced_embeddings = pca.fit_transform(frame_embeddings.numpy())
plt.figure(figsize=(8, 6))
plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1])
# Add frame numbers
for i, (x, y) in enumerate(reduced_embeddings):
plt.annotate(str(i), (x, y), fontsize=12)
plt.title("2D projection of frame embeddings")
plt.xlabel("Component 1")
plt.ylabel("Component 2")
plt.grid(True, linestyle='--', alpha=0.7)
plt.savefig("embedding_visualization.png")
plt.show()
def compute_frame_similarity(embeddings):
"""Compute cosine similarity between frame embeddings"""
# Mean over token dimension to get per-frame embeddings
frame_embeddings = embeddings.mean(dim=1).squeeze(0)
# Normalize embeddings
norm = frame_embeddings.norm(dim=1, keepdim=True)
normalized_embeddings = frame_embeddings / norm
# Compute similarity matrix
similarity = torch.mm(normalized_embeddings, normalized_embeddings.t())
# Visualize similarity matrix
plt.figure(figsize=(8, 6))
plt.imshow(similarity.numpy(), cmap='viridis')
plt.colorbar(label='Cosine Similarity')
plt.title("Frame-to-Frame Similarity")
plt.xlabel("Frame Index")
plt.ylabel("Frame Index")
plt.savefig("frame_similarity.png")
plt.show()
return similarity
def detect_scene_changes(similarity_matrix, threshold=0.8):
"""Simple scene change detection based on frame similarity"""
# Check if adjacent frames are below similarity threshold
scene_changes = []
sim_np = similarity_matrix.numpy()
for i in range(len(sim_np) - 1):
if sim_np[i, i+1] < threshold:
scene_changes.append(i+1)
print(f"Detected {len(scene_changes)} potential scene changes at frames: {scene_changes}")
return scene_changes
def main():
# Load model
feature_extractor, model = load_video_mae_model()
# Process video
video_path = "sample_video.mp4"
frames = extract_frames(video_path, num_frames=8, sample_rate=30)
# Get embeddings
video_embeddings, outputs = get_video_embeddings(feature_extractor, model, frames)
print("Video embeddings shape:", video_embeddings.shape) # [batch, frames, hidden_dim]
# Visualize frames and embeddings
visualize_frames_and_embeddings(frames, video_embeddings)
# Compute and visualize frame similarity
similarity = compute_frame_similarity(video_embeddings)
# Detect scene changes
scene_changes = detect_scene_changes(similarity, threshold=0.8)
print("Processing complete!")
if __name__ == "__main__":
main()
The example above demonstrates a comprehensive approach to working with videos in machine learning contexts using the VideoMAE (Video Masked Autoencoder) model. VideoMAE is a self-supervised learning framework for video understanding that works by reconstructing masked portions of video frames. Let's break down the key components:
Video Frame Extraction: The code uses the PyAV library to efficiently decode and extract frames from video files at specified intervals. This is crucial for video processing since working with every frame would be computationally expensive and often redundant, as adjacent frames typically contain similar information.
Feature Extraction with VideoMAE: The extracted frames are processed through VideoMAE, which transforms the raw pixel data into high-dimensional feature vectors (embeddings). These embeddings capture semantic information about objects, actions, and scenes present in the video.
Visualization Components: The code includes several visualization functions that help understand both the raw video content (displaying extracted frames) and the encoded representations (embedding visualizations). This is valuable for debugging and gaining insights into how the model "sees" the video.
Frame Similarity Analysis: By computing cosine similarity between frame embeddings, the code can identify how similar or different consecutive frames are. This has practical applications in scene boundary detection, content summarization, and keyframe extraction.
Scene Change Detection: A simple threshold-based approach is implemented to detect potential scene changes, which could be useful for video indexing, summarization, or creating chapter markers.
The code represents a foundation for more complex video understanding tasks like action recognition, video captioning, or video question answering. These capabilities are essential for applications ranging from content moderation and video search to assistive technologies for the visually impaired.
When working with the VideoMAE model, it's important to understand that:
- The model's input preprocessing is specific and requires frames to be in a particular format and dimension.
- The output embeddings are hierarchical and capture different levels of temporal and spatial information.
- The token dimension in the output shape represents the spatial tokens that the image is divided into.
- For downstream tasks, you would typically need to apply additional processing or fine-tuning to adapt these generic embeddings for specific purposes.
This code example provides a solid starting point for exploring multimodal capabilities that bridge the gap between computer vision and natural language processing, which is increasingly important as AI systems need to understand the world in ways that more closely resemble human perception.
5.3.5 Cross-Modal Reasoning
Cross-modal reasoning goes beyond processing modalities in isolation. It's about integration - the ability to synthesize and analyze information across different perceptual channels simultaneously. This represents a significant advancement over systems that can only process one type of input at a time, as it mirrors how humans naturally perceive and understand the world around them.
Unlike traditional AI systems that handle each input type separately, cross-modal models create a unified understanding by establishing connections between different types of information, enabling more comprehensive and contextual analysis. This integration happens at a deep representational level, where the model learns to map concepts across modalities into a shared semantic space.
For example, when a cross-modal system processes both an image of a dog and the word "dog," it doesn't treat these as separate, unrelated inputs. Instead, it recognizes they refer to the same concept despite coming through different perceptual channels. This ability to form these cross-modal associations is fundamental to human cognition and represents a crucial step toward more human-like AI understanding.
The technical implementation of cross-modal reasoning often involves complex neural architectures with shared embedding spaces, cross-attention mechanisms, and fusion techniques that preserve the unique characteristics of each modality while enabling information to flow between them. These systems must learn not just to process each modality effectively but to identify meaningful correlations between them, distinguishing relevant connections from coincidental ones.
Audio + Video:
Lip-reading and voice alignment, where models can match spoken words with mouth movements to improve speech recognition in noisy environments or for hearing-impaired users. This integration allows for more robust communication understanding. The system analyzes both the visual cues of lip movements and the acoustic properties of speech, compensating for deficiencies in either modality.
When processing lip movements, these systems track facial landmarks and mouth shapes that correspond to specific phonemes (speech sounds). Meanwhile, the audio component analyzes spectral and temporal features of the speech signal. By combining these streams of information, the system can disambiguate similar-sounding phonemes that have distinct visual representations (like "ba" vs "fa") or clarify unclear audio by leveraging the visual channel.
Advanced models employ attention mechanisms that dynamically weight the importance of visual versus audio inputs depending on their reliability. For instance, when ambient noise increases, the system automatically places greater emphasis on visual information. Conversely, in low-light conditions where visual data is less reliable, the audio channel receives higher priority.
This is particularly valuable in crowded settings where background noise might otherwise make speech recognition impossible, or in assistive technologies for people with hearing impairments who rely partly on visual cues for communication. In teleconferencing applications, this technology helps maintain clear communication even with unstable internet connections by reconstructing parts of the message from the available modality.
Example: Cross-Modal Reasoning: Audio + Video Integration
import torch
import torchaudio
import torch.nn as nn
import torch.nn.functional as F
import av
import numpy as np
import matplotlib.pyplot as plt
from transformers import Wav2Vec2Processor, Wav2Vec2Model
from transformers import VideoMAEFeatureExtractor, VideoMAEModel
from sklearn.metrics.pairwise import cosine_similarity
from PIL import Image
import librosa
import librosa.display
class AudioVideoSyncModel(nn.Module):
"""
A model for audio-video synchronization and cross-modal reasoning
"""
def __init__(self, audio_dim=768, video_dim=768, joint_dim=512):
super().__init__()
self.audio_projection = nn.Linear(audio_dim, joint_dim)
self.video_projection = nn.Linear(video_dim, joint_dim)
self.cross_attention = nn.MultiheadAttention(
embed_dim=joint_dim,
num_heads=8,
batch_first=True
)
self.classifier = nn.Sequential(
nn.Linear(joint_dim, 256),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(256, 1),
nn.Sigmoid()
)
def forward(self, audio_features, video_features):
"""
Process audio and video features and compute synchronization score
Args:
audio_features: Tensor of shape [batch_size, seq_len_audio, audio_dim]
video_features: Tensor of shape [batch_size, seq_len_video, video_dim]
Returns:
sync_score: Synchronization probability between 0-1
joint_features: Cross-modal features after attention
"""
# Project to common space
audio_proj = self.audio_projection(audio_features)
video_proj = self.video_projection(video_features)
# Apply cross-attention from video to audio
joint_features, _ = self.cross_attention(
query=video_proj,
key=audio_proj,
value=audio_proj
)
# Get global representation by mean pooling
global_joint = torch.mean(joint_features, dim=1)
# Predict synchronization score
sync_score = self.classifier(global_joint)
return sync_score, joint_features
def extract_video_frames(video_path, sample_rate=5):
"""
Extract frames from a video at regular intervals
Args:
video_path: Path to video file
sample_rate: Sample every nth frame
Returns:
List of frames as numpy arrays in RGB format
"""
frames = []
try:
container = av.open(video_path)
stream = container.streams.video[0]
total_frames = stream.frames
fps = float(stream.average_rate)
print(f"Video: {total_frames} frames, {fps} fps")
for i, frame in enumerate(container.decode(video=0)):
if i % sample_rate == 0:
# Convert to RGB numpy array
img = frame.to_ndarray(format='rgb24')
frames.append(img)
print(f"Extracted {len(frames)} frames")
container.close()
except Exception as e:
print(f"Error extracting video frames: {e}")
return frames
def extract_audio_from_video(video_path, target_sr=16000):
"""
Extract audio from a video file
Args:
video_path: Path to video file
target_sr: Target sampling rate
Returns:
Audio waveform and sample rate
"""
try:
container = av.open(video_path)
audio_stream = container.streams.audio[0]
# Initialize an empty numpy array to store audio samples
audio_data = []
# Decode audio
for frame in container.decode(audio=0):
# Convert PyAV AudioFrame to numpy array
frame_data = frame.to_ndarray()
audio_data.append(frame_data)
# Concatenate audio frames
if audio_data:
audio_array = np.concatenate(audio_data)
# Convert to mono if stereo
if len(audio_array.shape) > 1 and audio_array.shape[1] > 1:
audio_array = np.mean(audio_array, axis=1)
# Resample if needed
original_sr = audio_stream.rate
if original_sr != target_sr:
audio_resampled = librosa.resample(
audio_array,
orig_sr=original_sr,
target_sr=target_sr
)
return audio_resampled, target_sr
return audio_array, original_sr
else:
raise ValueError("No audio frames found")
except Exception as e:
print(f"Error extracting audio: {e}")
return None, None
def process_video(video_path, video_model, video_processor, sample_rate=5):
"""
Extract and process video frames
Args:
video_path: Path to video file
video_model: VideoMAE model
video_processor: VideoMAE feature extractor
sample_rate: Sample every nth frame
Returns:
Video features tensor
"""
# Extract frames
frames = extract_video_frames(video_path, sample_rate)
if not frames:
raise ValueError("No frames were extracted")
# Process frames with VideoMAE
inputs = video_processor(frames, return_tensors="pt")
with torch.no_grad():
outputs = video_model(**inputs)
video_features = outputs.last_hidden_state
return video_features, frames
def process_audio(audio_array, sr, audio_model, audio_processor):
"""
Process audio with Wav2Vec2
Args:
audio_array: Audio samples as numpy array
sr: Sample rate
audio_model: Wav2Vec2 model
audio_processor: Wav2Vec2 processor
Returns:
Audio features tensor
"""
# Prepare audio for Wav2Vec2
inputs = audio_processor(
audio_array,
sampling_rate=sr,
return_tensors="pt"
)
with torch.no_grad():
outputs = audio_model(**inputs)
audio_features = outputs.last_hidden_state
return audio_features
def detect_audiovisual_sync(sync_scores, threshold=0.5):
"""
Analyze synchronization scores to detect in-sync vs out-of-sync segments
Args:
sync_scores: List of synchronization scores
threshold: Threshold for considering audio-video in sync
Returns:
List of in-sync and out-of-sync segments
"""
segments = []
current_segment = {"start": 0, "status": "sync" if sync_scores[0] >= threshold else "out-of-sync"}
for i in range(1, len(sync_scores)):
current_status = "sync" if sync_scores[i] >= threshold else "out-of-sync"
previous_status = "sync" if sync_scores[i-1] >= threshold else "out-of-sync"
if current_status != previous_status:
# End the previous segment
current_segment["end"] = i - 1
segments.append(current_segment)
# Start a new segment
current_segment = {"start": i, "status": current_status}
# Add the final segment
current_segment["end"] = len(sync_scores) - 1
segments.append(current_segment)
return segments
def visualize_sync_analysis(frames, audio_waveform, sr, sync_scores, segments):
"""
Visualize audio, video frames, and synchronization analysis
Args:
frames: List of video frames
audio_waveform: Audio samples
sr: Audio sample rate
sync_scores: Synchronization scores
segments: Detected sync/out-of-sync segments
"""
fig, axes = plt.subplots(3, 1, figsize=(15, 10), gridspec_kw={'height_ratios': [1, 1, 2]})
# Plot synchronization scores
axes[0].plot(sync_scores)
axes[0].set_ylim(0, 1)
axes[0].set_ylabel('Sync Score')
axes[0].set_xlabel('Frame')
axes[0].axhline(y=0.5, color='r', linestyle='--')
# Highlight sync/out-of-sync segments
for segment in segments:
color = 'green' if segment['status'] == 'sync' else 'red'
axes[0].axvspan(segment['start'], segment['end'], alpha=0.2, color=color)
# Plot audio waveform
librosa.display.waveshow(audio_waveform, sr=sr, ax=axes[1])
axes[1].set_ylabel('Amplitude')
# Display frames at key points
n_frames = min(8, len(frames))
indices = np.linspace(0, len(frames)-1, n_frames, dtype=int)
for i, idx in enumerate(indices):
ax = plt.subplot(3, n_frames, i + 2*n_frames + 1)
ax.imshow(frames[idx])
ax.set_title(f"Frame {idx}")
ax.axis('off')
plt.tight_layout()
plt.savefig('av_sync_analysis.png')
plt.show()
def demonstrate_lip_reading(sync_model, audio_features, video_features, frames):
"""
Demonstrate lip-reading by finding the most relevant audio for each video frame
using the cross-attention mechanism
Args:
sync_model: Trained AudioVideoSyncModel
audio_features: Audio features tensor
video_features: Video features tensor
frames: List of video frames
Returns:
Attention weights showing audio-visual connections
"""
# Project features to common space
audio_proj = sync_model.audio_projection(audio_features)
video_proj = sync_model.video_projection(video_features)
# Compute raw attention scores
attn_weights = torch.matmul(video_proj, audio_proj.transpose(-2, -1)) / np.sqrt(audio_proj.size(-1))
# Convert to probabilities
attn_probs = F.softmax(attn_weights, dim=-1)
# Visualize attention for selected frames
n_frames = min(4, len(frames))
indices = np.linspace(0, len(frames)-1, n_frames, dtype=int)
fig, axes = plt.subplots(2, n_frames, figsize=(15, 6))
for i, idx in enumerate(indices):
# Show the frame
axes[0, i].imshow(frames[idx])
axes[0, i].set_title(f"Frame {idx}")
axes[0, i].axis('off')
# Show attention weights (which audio segments this frame attends to)
if idx < attn_probs.shape[1]: # Ensure index is valid
axes[1, i].plot(attn_probs[0, idx].detach().numpy())
axes[1, i].set_title("Audio Attention")
axes[1, i].set_xlabel("Audio Frames")
axes[1, i].set_ylabel("Attention Weight")
plt.tight_layout()
plt.savefig('lip_reading_attention.png')
plt.show()
return attn_probs
def main():
# Initialize models
print("Loading models...")
# Audio model (Wav2Vec2)
audio_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
audio_model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
# Video model (VideoMAE)
video_processor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
video_model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
# Cross-modal synchronization model
sync_model = AudioVideoSyncModel(
audio_dim=768, # Wav2Vec2 feature dimension
video_dim=768, # VideoMAE feature dimension
joint_dim=512 # Joint embedding dimension
)
# Process video with speaking person
video_path = "speaking_person.mp4"
print(f"Processing video: {video_path}")
# Extract audio from video
audio_array, sr = extract_audio_from_video(video_path)
if audio_array is None:
print("Failed to extract audio")
return
print(f"Audio: {len(audio_array)} samples, {sr} Hz")
# Process video frames
video_features, frames = process_video(
video_path,
video_model,
video_processor,
sample_rate=5
)
# Process audio
audio_features = process_audio(
audio_array,
sr,
audio_model,
audio_processor
)
print("Audio features shape:", audio_features.shape)
print("Video features shape:", video_features.shape)
# Simulate training the sync model (in practice, this requires proper training)
# Here we're just demonstrating the forward pass
sync_scores = []
step_size = max(1, audio_features.shape[1] // video_features.shape[1])
for i in range(video_features.shape[1]):
# Get corresponding audio chunk
start_idx = i * step_size
end_idx = min((i + 1) * step_size, audio_features.shape[1])
audio_chunk = audio_features[:, start_idx:end_idx, :]
video_frame_feat = video_features[:, i:i+1, :]
# Mean pool audio chunk
audio_chunk_pooled = torch.mean(audio_chunk, dim=1, keepdim=True)
# Get sync score
score, _ = sync_model(audio_chunk_pooled, video_frame_feat)
sync_scores.append(score.item())
# Analyze synchronization
segments = detect_audiovisual_sync(sync_scores)
print("Detected segments:")
for segment in segments:
print(f"Frames {segment['start']}-{segment['end']}: {segment['status']}")
# Visualize results
visualize_sync_analysis(frames, audio_array, sr, sync_scores, segments)
# Demonstrate lip reading capabilities
print("Generating lip reading visualization...")
attn_weights = demonstrate_lip_reading(sync_model, audio_features, video_features, frames)
print("Analysis complete!")
if __name__ == "__main__":
main()
This code example demonstrates a comprehensive approach to cross-modal reasoning with audio and video, focusing on lip-reading and speech-video synchronization analysis. Let's break down the key components:
The AudioVideoSyncModel class implements a neural network architecture that processes both audio and video features and learns to align them in a shared representation space. It uses several important mechanisms:
- Modal-specific projection layers that map audio and video features to a common semantic space
- Cross-attention mechanisms that allow the model to determine which parts of the audio correspond to which visual frames
- A classification head that predicts whether audio and video are synchronized
The extract_video_frames function extracts frames from a video at regular intervals using PyAV, which provides a Pythonic binding to the FFmpeg libraries. This sampling approach is essential for efficiency since processing every frame would be computationally expensive and often redundant for semantic understanding.
Similarly, extract_audio_from_video extracts the audio track from a video file and processes it into a format suitable for deep learning models, including converting to a consistent sampling rate and handling multi-channel audio.
The process_video and process_audio functions use pretrained models from the Transformers library to convert raw video frames and audio signals into high-dimensional feature representations:
- VideoMAE (Video Masked Autoencoder) processes video frames, extracting features that capture objects, actions, and visual context
- Wav2Vec2 processes audio, capturing phonetic and linguistic information
The detect_audiovisual_sync function analyzes the synchronization scores to identify segments where audio and video are well-aligned versus segments where they might be out of sync. This is valuable for applications like automatic correction of audio-video synchronization issues in recorded content.
The visualize_sync_analysis function creates a comprehensive visualization showing:
- The synchronization scores over time
- Color-coded segments indicating in-sync and out-of-sync portions
- The audio waveform
- Key video frames from throughout the sequence
The demonstrate_lip_reading function shows how the cross-attention mechanism effectively implements a form of lip reading by connecting mouth movements in video frames with corresponding audio segments. It visualizes the attention weights, showing which parts of the audio each video frame is most strongly associated with.
In the main function, we see the entire pipeline in action:
- Models are loaded and initialized
- Video and audio are extracted and processed
- The synchronization model is applied to analyze the alignment between modalities
- Results are visualized for interpretation
This implementation has numerous practical applications:
- Assistive technology for hearing-impaired users to enhance speech understanding with visual cues
- Video production tools that automatically detect and correct audio-video synchronization issues
- Enhanced speech recognition in noisy environments by leveraging visual information
- Security applications for detecting manipulated content where audio and video don't naturally align
- Educational tools that ensure properly synchronized content for optimal learning experiences
The example represents a foundation that could be extended with more sophisticated training procedures and architectural improvements. In a production environment, this system would require proper training data consisting of paired audio-video examples with both synchronized and deliberately misaligned samples.
Text + Image
Answering questions about a chart or photo, which requires understanding both the visual elements (colors, shapes, spatial relationships) and textual context to provide meaningful responses. This capability enables more intuitive data exploration and visual information retrieval. The model must recognize visual patterns, understand spatial arrangements, and interpret color encodings while simultaneously processing textual labels and contextual information provided alongside the image. This visual-textual integration requires sophisticated neural architectures that can maintain representations from both modalities and reason across them effectively.
For example, when analyzing a financial chart, the system must understand not only the visual representation of data trends but also interpret labels, legends, and axes to provide accurate insights. It needs to recognize different chart types (bar charts, line graphs, pie charts), understand what each visual element represents (rising trends, market segments, comparative data), and correctly interpret numerical scales and time periods. The system must also discern the significance of color coding (e.g., red for losses, green for gains) and pattern variations (e.g., dotted lines for projections versus solid lines for historical data), while connecting these visual cues to financial terminology and concepts in the accompanying text.
Similarly, in medical imaging, a cross-modal system can correlate visual patterns in scans with textual patient records to assist in diagnosis or treatment planning. This requires identifying subtle visual anomalies in X-rays, MRIs, or CT scans while simultaneously considering patient history, symptoms, and other clinical notes to provide contextually relevant medical analysis. The system must recognize anatomical structures, detect abnormalities like fractures, tumors, or inflammation, and understand how these visual findings relate to symptoms described in textual records. This integration enables more comprehensive clinical decision support by connecting what is seen in the image with what is known about the patient's condition.
This integration of visual and textual information also extends to other domains like geospatial analysis (interpreting maps alongside location descriptions), document understanding (processing diagrams with explanatory text), and educational content (connecting visual teaching aids with textual explanations). In geospatial applications, models must understand geographical features, topographical elements, and symbolic representations on maps while relating them to textual location descriptions, directions, or demographic data. For document understanding, the system needs to parse complex layouts with mixed text and visuals, comprehending how diagrams illustrate concepts explained in accompanying text.
The true power of multimodal systems emerges when they can seamlessly blend these different information streams into a unified understanding, allowing for more natural human-AI interaction across diverse applications. This unified comprehension enables AI systems to provide more contextually appropriate responses that consider the full range of available information, similar to how humans integrate multiple sensory inputs when understanding their environment. Through techniques like cross-attention and joint embedding spaces, these models create rich representations that capture the relationships between words and visual elements, enabling more sophisticated reasoning that mirrors human cognitive processes.
Text + Video
Explaining an event in a clip or summarizing a documentary, which demands temporal reasoning across frames while connecting visual elements to narrative structure. This integration supports content analysis at a much deeper semantic level than static image understanding, as it requires processing sequential information and understanding how scenes evolve over time. The AI must analyze multiple dimensions simultaneously - visual composition, motion patterns, temporal transitions, audio cues, and narrative progression - to construct a coherent understanding of the content.
The system must track objects and actors across time, understand causality between events, and connect visual sequences with contextual information. This tracking involves sophisticated computer vision algorithms that can maintain object identity despite changes in appearance, lighting, camera angle, or partial occlusion. For example, when analyzing a nature documentary, the model needs to recognize not just individual animals, but follow their movements across different shots, understand the narrative arc (such as a predator stalking prey), and connect these visual sequences with the documentary's educational themes. The system must interpret both explicit visual information (what is directly shown) and implicit content (what is suggested or implied through editing techniques, camera movements, or juxtaposition of scenes).
Temporal reasoning also requires understanding cinematic language - how cuts, transitions, establishing shots, close-ups, and montages contribute to storytelling. The model must recognize when a flashback occurs, when parallel storylines are being presented, or when a montage compresses time. Similarly, for news footage, the system must recognize key figures, understand the chronology of events, and place them within the broader context provided by narration or interviews. This involves correlating spoken information with visual evidence, distinguishing between primary footage and archival material, and recognizing when the same event is shown from multiple perspectives.
This multimodal reasoning enables applications like automatically generating detailed video summaries that capture both visual content and narrative structure, creating accessible descriptions for visually impaired users that convey the emotional and storytelling elements of video content, or analyzing surveillance footage with textual reports to identify specific incidents by matching visual patterns with textual descriptions of events. These applications require not just object recognition but scene understanding - comprehending the relationships between objects, their interactions, and how these elements combine to create meaning.
Advanced systems can even identify emotional arcs in film by correlating visual cinematography techniques with dialogue and music to understand how directors convey meaning through multiple channels simultaneously. This includes analyzing color grading (how warm or cool tones evoke different emotions), camera movement (steady vs. handheld to convey stability or tension), lighting techniques (high-key vs. low-key for different moods), and how these visual elements synchronize with musical cues, sound effects, and dialogue to create a unified emotional experience.
The ultimate goal is to develop AI systems that can "watch" and "understand" video content with a level of comprehension approaching that of human viewers, interpreting both denotative content (what is literally shown) and connotative meaning (what is symbolically or emotionally conveyed).
Example Use Cases:
- Accessibility: automatic captioning of lectures that combines audio transcription with slide descriptions, making educational content more accessible to people with hearing impairments or those learning in noisy environments. The system must synchronize the verbal explanation with relevant visual content.
This requires sophisticated speech recognition that can handle technical terminology and different accents, while also identifying the context of what's being discussed by analyzing visual slides. The technology must accurately timestamp speech to align with corresponding visual elements, creating a seamless experience that mimics how in-person attendees process the lecture.
- Education: tutoring systems that can explain a diagram while narrating, creating more engaging and comprehensive learning experiences by linking visual concepts with verbal explanations. These systems can adapt to different learning styles and provide multimodal reinforcement of complex concepts.
For example, when teaching molecular biology, the system could highlight specific parts of a cell diagram while verbally explaining their functions, then dynamically adjust its teaching approach based on student comprehension signals. This multimodal approach helps students form stronger mental models by connecting abstract concepts with visual representations, significantly enhancing knowledge retention compared to single-mode instruction.
- Robotics: interpreting both visual signals and verbal instructions simultaneously, enabling more natural human-robot interaction in collaborative environments. This allows robots to understand contextual commands like "pick up the red cup on the left" by combining vision processing with language understanding.
This integration is critical for assistive robots in healthcare, manufacturing, and household settings, where they must navigate complex, dynamic environments while responding to human directives that reference objects in physical space. Advanced systems can also interpret human gestures, facial expressions, and environmental cues alongside verbal commands, creating more intuitive and efficient human-robot collaboration that doesn't require humans to adapt their natural communication style.
5.3.6 Why This Matters
Video adds the dimension of time, allowing AI to model cause and effect. This temporal dimension enables AI systems to understand sequences of events, track objects through space, and recognize patterns that unfold over time. Unlike static images, video provides context about how actions lead to consequences, how objects interact, and how scenes transform. When processing video, AI can analyze motion trajectories, temporal correlations, and dynamic changes that reveal deeper insights about physical phenomena and behavioral patterns.
This capability is crucial for applications like autonomous driving (predicting pedestrian movements based on gait patterns and historical trajectory), security systems (detecting unusual behavior patterns by comparing current activities against established norms), and healthcare (analyzing patient movements in physical therapy to assess recovery progress and provide real-time feedback). The temporal reasoning enabled by video analysis allows AI to understand not just what is happening in a single moment, but how events unfold over time, creating a more complete understanding of complex scenarios.
By processing multiple frames in sequence, AI can learn to anticipate what might happen next based on what it has observed, similar to how humans develop intuitive physics. This predictive capability stems from the model's ability to extract temporal dependencies between consecutive frames, identifying cause-effect relationships and recurring patterns.
For example, in sports analysis, AI can predict player movements based on historical behavior, while in weather forecasting, it can identify evolving cloud formations that indicate changing weather conditions. This temporal understanding is fundamental to creating AI systems that can interact meaningfully with our dynamic world.
Cross-modal reasoning allows AI to integrate multiple senses, mirroring human perception. Just as humans simultaneously process what they see, hear, and read to form a complete understanding of their environment, multimodal AI systems can correlate information across different input types. This integration enables more robust understanding - when one modality provides unclear information, others can compensate. This capability represents a fundamental shift from traditional AI systems that process each sensory input independently to a more holistic approach that considers the relationships and interdependencies between different forms of information.
The power of cross-modal reasoning lies in its ability to leverage complementary information from different sources, similar to how humans instinctively combine multiple sensory inputs to navigate complex environments. By establishing correlations between visual patterns, auditory signals, and textual descriptions, AI systems can develop a more nuanced understanding of the world that transcends the limitations of any single modality. This approach allows the system to be more resilient to noise or ambiguity in individual channels by drawing on the strengths of other available inputs.
For example, in noisy environments, visual lip reading can enhance speech recognition, while in visually complex scenes, audio cues can help identify important elements. In clinical settings, AI systems can correlate medical images with written patient histories and verbal descriptions from healthcare providers to form more comprehensive diagnostic assessments. During video conference analysis, the system can integrate facial expressions, voice tone, and textual chat to better understand participant engagement and emotional states.
This cross-modal reasoning also allows AI to understand concepts more deeply by connecting abstract descriptions (text) with concrete sensory experiences (images, sounds), creating richer mental representations that more closely resemble human understanding. When an AI system can connect the textual description of "rustling leaves" with both visual imagery of moving foliage and the corresponding audio, it develops a more complete conceptual understanding than would be possible through any single modality alone. This multi-dimensional representation enables more sophisticated reasoning about real-world scenarios and more intuitive interaction with human users.
Together, these directions push LLMs closer to being general AI assistants, not just text predictors. By expanding beyond text-only processing, these systems can interact with the world more naturally and comprehensively. They can analyze and discuss visual content, process information that unfolds over time, understand speech in context, and integrate these diverse inputs into coherent responses.
This broader perception allows AI assistants to handle tasks that require understanding the physical world - from helping visually impaired users navigate environments to assisting professionals in analyzing complex multimodal data like medical scans with patient histories. The evolution toward true multimodal understanding represents a significant step toward AI systems that can perceive and reason about the world in ways that more closely align with human cognitive capabilities.
5.3.7 Looking Ahead
The frontier of multimodal AI is moving toward true integration: models that seamlessly blend text, vision, audio, and video in a single framework. This represents a significant evolution beyond current approaches where separate models handle different modalities or where models specialize in specific pairings like text-image or audio-text. True integration means developing neural architectures that process all modalities simultaneously through shared attention mechanisms and unified embedding spaces, allowing information to flow freely across different sensory channels.
These integrated models can process multiple input streams in parallel while maintaining awareness of how they relate to each other contextually. For example, understanding that a speaker's gestures on video correspond to specific concepts mentioned in their speech, or that a diagram shown in a presentation directly illustrates a verbal explanation. This cross-modal attention enables much richer understanding than processing each stream independently.
Instead of switching between specialized systems, one unified model could seamlessly process and analyze multiple forms of content simultaneously, providing a truly integrated understanding:
- Watch a video lecture, tracking visual demonstrations, facial expressions, and board work. This visual processing would include recognizing the instructor's gestures that emphasize key points, identifying when they're directing attention to specific areas, and understanding visual demonstrations that illustrate complex concepts. The model would also track changes on boards or screens, understanding how written content evolves over time.
- Listen to the narration, including tonal emphasis, pauses, and verbal cues that signal important concepts. This audio processing would detect changes in vocal pitch and volume that indicate emphasis, recognize rhetorical questions versus literal ones, understand when pauses signal transitions between topics, and identify verbal markers like "importantly" or "remember this" that highlight critical information.
- Read the slides, processing textual content, diagrams, charts, and their spatial relationships. This would involve understanding how bullet points relate hierarchically, interpreting complex visualizations like flowcharts or graphs, recognizing when text labels correspond to visual elements, and comprehending how the spatial arrangement of information conveys structural relationships between concepts.
- Summarize everything in plain English, integrating insights from all modalities into a coherent narrative. This would combine information from all sources, resolving conflicts when different modalities present contradictory information, prioritizing content based on emphasis across modalities, and presenting a unified understanding that captures the essential knowledge from all sources in a human-readable format.
These capabilities go far beyond simple feature extraction from different modalities. They represent a fundamental shift in how AI systems process and integrate information across sensory channels. While traditional multimodal systems might separately process text, images, and audio before combining their outputs, truly integrated multimodal models employ sophisticated cross-attention mechanisms that allow information to flow bidirectionally between modalities throughout the entire processing pipeline.
These cross-attention mechanisms enable several critical functions: They can dynamically align corresponding elements across modalities (matching spoken words with relevant visual objects), establish semantic connections between different representations of the same concept (connecting the word "dog" with both its visual appearance and the sound of barking), and detect discrepancies when information from different modalities appears contradictory (recognizing when spoken instructions conflict with visual demonstrations).
The resolution of these complex relationships into a unified understanding requires models to develop abstract representations that capture meaning independently of the source modality. This allows the system to identify when information in one modality complements, reinforces, or contradicts information in another, and to make reasoned judgments about how to integrate these various inputs.
For instance, when a lecturer says "as you can see in this graph" while pointing to a chart, the model must perform a complex series of operations: it must process the audio to extract the verbal reference, track the physical gesture through visual processing, identify the chart as the object being referenced, analyze the chart's content, and then integrate all this information into a coherent semantic representation that connects the verbal explanation with the visual data. This requires temporal alignment (matching when words are spoken with when gestures occur), spatial alignment (connecting the gesture to the specific area of the chart), and semantic alignment (understanding how the spoken explanation relates to the visual information).
These are the kinds of sophisticated capabilities being pioneered in research labs today through several innovative approaches:
Multiway transformers that process different modalities in parallel while allowing attention to flow between them, enabling each modality to influence how others are processed. These architectures extend the traditional transformer design by implementing specialized encoding pathways for each modality (text, image, audio, video) while maintaining cross-modal attention mechanisms. For example, when processing a video lecture, the visual pathway might attend to important visual elements while simultaneously receiving attention signals from the audio pathway that processes the speaker's voice, creating a dynamic feedback loop between modalities.
Shared embedding spaces that map inputs from different modalities into a common representational format where relationships between concepts can be directly compared regardless of their source. These unified semantic spaces enable the model to recognize that the word "apple," an image of an apple, and the sound of someone biting into an apple all refer to the same underlying concept. This approach creates a language-agnostic representation that captures meaning beyond the surface-level characteristics of any particular modality, allowing the model to transfer knowledge across modalities and reason about concepts at an abstract level.
Contrastive learning techniques that teach models to recognize when different modal representations refer to the same underlying concept by bringing their embeddings closer together in the shared space. These methods work by training the model to minimize the distance between representations of semantically related inputs (like an image of a dog and the text "a golden retriever playing") while maximizing the distance between unrelated inputs. Advanced implementations use techniques like CLIP (Contrastive Language-Image Pre-training), which learns powerful visual representations by training on millions of image-text pairs, enabling zero-shot recognition of visual concepts based on their textual descriptions.
These approaches are further enhanced by techniques like cross-modal attention masking (selectively focusing on relevant parts of each modality), modality-specific preprocessing layers (handling the unique characteristics of each input type), and sophisticated alignment strategies that synchronize temporal information across modalities with different sampling rates.
Together, these advanced architectural innovations hint at the future of cross-sensory intelligence - AI systems that can perceive and process information in ways that more closely resemble human cognition, where our understanding of the world emerges from the integration of all our senses working in concert. This holistic processing allows for more robust comprehension that leverages complementary information across modalities, enables more natural human-AI interaction that doesn't require humans to adapt their communication style to the system, and supports more sophisticated reasoning about real-world situations that inherently involve multiple sensory dimensions.
5.3 Video and Cross-Modal Research Directions
So far, we've seen how multimodal models can read text, interpret images, and even listen to audio. But the real world unfolds in time. Understanding video means not just recognizing objects in frames, but reasoning about motion, causality, and events across time. This temporal dimension adds tremendous complexity because it requires models to track entities as they move, change, appear, and disappear throughout a sequence.
For instance, understanding a cooking video requires tracking ingredients as they transform through various stages of preparation. Similarly, comprehending sports footage demands following players as they move across a field and interact with each other and the ball. These capabilities go far beyond static image recognition, requiring the model to maintain a coherent understanding of objects' identities and relationships as they evolve through time.
At the same time, many real-world tasks require more than one sense at once. A teacher may speak while showing slides, or a person may gesture while giving instructions. This is where cross-modal reasoning becomes essential — connecting multiple streams of input into a unified interpretation. Humans naturally integrate information across senses, allowing us to connect a speaker's voice to their facial movements, associate sounds with their visual sources, and understand demonstrations that combine verbal explanations with visual examples.
Creating AI systems with this capability requires sophisticated architectures that can not only process each modality effectively but also align and integrate information across them. For example, when watching an instructional video, the system must synchronize the spoken narration with the corresponding visual demonstrations, understanding which words refer to which objects or actions on screen. This form of cross-modal grounding is fundamental to creating truly helpful assistants that can understand and engage with the world as humans do.
5.3.1 Video Understanding with Transformers
Unlike images, video is a sequence of frames — and sequences are exactly what transformers excel at. The challenge is that video sequences are often huge: a 10-second clip at 30 FPS has 300 frames. Feeding all of that directly into a transformer would be computationally impossible with current hardware limitations.
For context, many image-based transformers struggle with processing even a few high-resolution images simultaneously, so handling hundreds of sequential frames would require exponentially more memory and processing power. The computational complexity grows quadratically with sequence length due to the self-attention mechanism, where each token must attend to every other token. With video, this problem is magnified dramatically.
To illustrate the scale of this challenge: if a single high-resolution frame requires processing 1,024 tokens (a modest estimate), then a 10-second video at 30 FPS would need to process 307,200 tokens simultaneously. The self-attention computation for this would involve approximately 94 billion token-to-token comparisons. Even with modern GPUs and TPUs, this is prohibitively expensive in terms of both memory requirements and computational time.
This computational burden becomes even more significant when we consider real-world applications. For instance, video analysis for security surveillance might involve processing hours of footage, potentially generating millions of frames. Similarly, content moderation systems for social media platforms need to analyze thousands of videos uploaded every minute. The sheer volume of data makes naive approaches to video processing with transformers unfeasible.
The memory requirements also present a substantial barrier. Self-attention matrices grow quadratically with input length, so a video with twice as many frames requires four times the memory. Modern GPUs typically have 16-80GB of VRAM, which would be quickly exhausted by even modest-length videos processed at full resolution. This memory constraint has forced researchers to develop specialized architectures and optimization techniques specifically for video understanding.
Additionally, video data presents unique temporal dependencies that span across frames. While a transformer could theoretically capture these relationships, the sheer volume of cross-frame connections creates a computational bottleneck that requires innovative architectural solutions beyond simply scaling up existing image transformer models.
Techniques to Handle Video
Frame sampling
Select only key frames or use a sliding window. This approach reduces computational load by choosing representative frames at regular intervals (e.g., every 5th frame) or focusing on frames with significant visual changes. While this sacrifices some temporal detail, it captures the essential content while making processing feasible.
Frame sampling is particularly effective when videos contain redundant information across consecutive frames. For example, in a surveillance video where the scene remains mostly static, processing every frame would be wasteful. By intelligently selecting frames, models can maintain high accuracy while dramatically reducing processing requirements.
The selection process can employ various strategies beyond simple fixed-interval sampling. Adaptive sampling techniques can analyze motion vectors or pixel differences between frames to determine when important changes occur. This allows more frames to be sampled during high-action sequences and fewer during static scenes, optimizing the information-to-computation ratio.
Additionally, sliding window approaches maintain temporal continuity by processing overlapping sets of frames. Rather than treating each frame in isolation, these methods analyze short sequences (e.g., 8-16 frames) at a time, sliding the window forward to progress through the video. This preserves short-term temporal relationships while keeping computation manageable.
In more detail, frame sampling works by strategically selecting a subset of frames from the complete video sequence. There are several methods for this selection, each with its own advantages for different video analysis scenarios:
- Uniform sampling: Taking frames at fixed intervals (e.g., one frame per second) to provide an even representation across the entire video. This approach is computationally efficient and works well for videos with consistent action or gradual changes. Uniform sampling reduces the computational burden by processing only a fraction of the total frames while maintaining temporal coverage across the entire video duration.When implementing uniform sampling, researchers typically define a sampling rate based on factors like video length, content type, and available computational resources.
For instance, action-heavy videos might require higher sampling rates (e.g., 2-3 frames per second) to capture quick movements, while slow-changing scenes might need only one frame every few seconds.The main advantage of uniform sampling is its simplicity and predictability. Since frames are selected at regular intervals, the model receives a consistent temporal distribution that spans the entire video without bias toward any particular segment. This helps prevent overfitting to specific temporal regions and ensures the model learns patterns that generalize across the entire timeline.
For example, in a wildlife documentary tracking animal migration, capturing one frame every few seconds can adequately represent the overall movement patterns while significantly reducing processing requirements. This approach would effectively showcase the gradual progression of herds across landscapes without needing to process every minute detail of movement between consecutive frames. The sampling rate can be adjusted based on the speed of migration – faster movements might require more frequent sampling, while slower journeys could be represented with fewer frames.
- Content-aware sampling: Using algorithms to detect significant visual changes and selecting frames only when meaningful transitions occur. This is particularly useful for videos with static scenes interrupted by important events.These methods analyze frame-to-frame differences in features like color histograms, edge patterns, or motion vectors to identify when something interesting happens. In surveillance footage, for instance, this approach might capture frames only when a person enters the frame, ignoring long periods where nothing changes.Content-aware sampling works by establishing baseline metrics for the visual content, then continuously monitoring for deviations that exceed predefined thresholds.
For example, the system might calculate the pixel-wise difference between consecutive frames, the change in distribution of colors, or the emergence of new edge patterns that could indicate new objects.More sophisticated implementations use computer vision techniques such as object detection and tracking to identify semantically meaningful changes. Rather than just measuring raw pixel differences, these systems can recognize when a new person appears, when an object moves significantly, or when the overall scene composition changes.
The computational efficiency gained through content-aware sampling can be dramatic. In a typical 24-hour surveillance video where activity occurs for only 30 minutes total, this approach might reduce the processing load by 97%, while still capturing all relevant events. This makes real-time video analysis feasible even with limited computing resources.Beyond surveillance, content-aware sampling proves valuable in domains like autonomous driving (capturing frames when traffic conditions change), medical monitoring (detecting significant patient movements), and sports analytics (identifying key plays in lengthy game footage).
- Keyframe extraction: Identifying frames that contain the most representative or information-rich content, often based on visual features or scene boundaries. These algorithms use techniques like clustering, where frames are grouped based on visual similarity, and the most central frame from each cluster is selected. This approach effectively condenses videos into their essential visual components while discarding redundant or transitional frames.
The clustering process typically involves converting each frame into feature vectors using techniques like convolutional neural networks (CNNs), then applying algorithms such as k-means or hierarchical clustering to group similar frames. Once clusters are formed, the frame closest to each cluster's centroid is selected as the keyframe, providing a diverse yet comprehensive sampling of the video's visual content.
For example, in a 30-minute documentary, keyframe extraction might identify just 20-30 frames that collectively represent all the major scenes, locations, and subjects, drastically reducing the processing requirements while preserving the core visual narrative.
Advanced methods may incorporate semantic understanding to identify frames that best capture the narrative elements of a video, such as those showing critical actions in a sports highlight or key emotional moments in a movie scene. These approaches go beyond low-level visual features to consider higher-level concepts like object interactions, facial expressions, and scene composition.
Modern keyframe extraction systems often employ deep learning models trained to recognize important visual moments based on millions of human-annotated videos. This allows them to prioritize frames with storytelling significance rather than just visual distinctiveness. For instance, in an interview video, the system might select frames showing important gestures or facial reactions rather than visually different but narratively insignificant background changes.
Some systems also incorporate additional contextual cues like audio peaks, subtitle changes, or scene transitions to better identify moments of importance. This multimodal approach ensures that keyframes align with significant developments in the video's content rather than just visual variations.
The computational benefits are substantial. For example, processing just 10% of frames can reduce memory requirements by 90% and computation time by a similar amount. This makes previously impossible tasks manageable with current hardware.
However, there are tradeoffs to consider. Fast-moving objects might appear to "teleport" between sampled frames, and subtle movements might be missed entirely. Researchers mitigate these issues by combining frame sampling with optical flow estimation or interpolation techniques that can reconstruct information about the skipped frames.
Temporal embeddings
Add position encodings for time as well as space. This technique extends the transformer's position embeddings to include temporal information, allowing the model to understand both where objects are located within frames and how they move across frames. These encodings help the model distinguish between identical frames appearing at different points in a sequence.
Temporal embeddings are crucial for video understanding because they provide essential context about when events occur in a sequence. Just as spatial position embeddings help transformers understand the arrangement of elements within an image, temporal embeddings encode the chronological order and relative timing of frames. This temporal awareness is particularly important when analyzing activities that unfold over time, like a person throwing a ball or a car turning at an intersection.
Without such temporal context, a model would struggle to differentiate between similar-looking frames that appear at different times in a video. For instance, in a cooking video where ingredients are added to a pot multiple times, the model needs to understand the sequence of additions to correctly interpret the recipe steps. Temporal embeddings provide this critical ordering information.
These embeddings can be implemented in several ways. One approach uses sinusoidal functions similar to those in the original transformer architecture, but with an additional dimension for time. Another method employs learnable embeddings specifically trained to capture temporal relationships. Some advanced systems use a combination of absolute time position (the frame's position in the entire sequence) and relative timing information (how far apart frames are from each other).
The sinusoidal approach has the advantage of being able to generalize to sequence lengths not seen during training, while learnable embeddings often capture more nuanced temporal patterns but may struggle with very long sequences. Researchers often experiment with both approaches to find the optimal solution for specific video understanding tasks.
Some advanced implementations also incorporate temporal embeddings at multiple scales. For instance, they might encode information about a frame's position within a second, a minute, and the entire video. This multi-scale approach helps models understand both fine-grained actions and longer narrative arcs within videos.
For example, in a video of a basketball game, temporal embeddings would help the model recognize that a player jumping, then releasing a ball, followed by the ball moving through a hoop represents a shooting sequence. Without temporal embeddings, these frames might be interpreted as disconnected events rather than a coherent action. The embeddings provide the critical temporal context that links these frames into a meaningful sequence.
Similarly, in a surveillance video, temporal embeddings allow the model to track individuals across frames and understand the progression of activities. This capability is essential for applications like activity recognition, where the order of actions defines the activity (e.g., entering a building versus leaving it involves the same frames in reverse order).
Hierarchical modeling
First process frames locally, then reason globally across segments. This multi-level approach initially treats smaller chunks of consecutive frames as units for local processing, extracting features about motion and changes. This hierarchical structure mirrors how humans understand videos - we first comprehend small actions and then connect them into larger narratives. The hierarchical approach is inspired by cognitive science research showing that human perception operates at multiple temporal scales simultaneously, from millisecond reactions to minute-long comprehension of complex scenes.
At the local level, models typically process 8-16 consecutive frames using lightweight attention mechanisms. This allows the model to capture short-term dynamics like object movement, facial expressions, or scene transitions without requiring extensive computational resources. These local processors extract rich representations that summarize what's happening in each small segment of video. The temporal receptive field at this level is carefully balanced - too few frames would miss important motion patterns, while too many would increase computational burden exponentially. Research has shown that 8-16 frames typically provides sufficient context to identify atomic actions while remaining computationally feasible.
These local processors employ specialized architectures like factorized attention or 3D convolutions that efficiently model spatiotemporal relationships. Some implementations use causal masking to ensure the model only attends to current and past frames, enabling real-time processing for applications like autonomous driving or security monitoring. Others process bidirectionally to maximize information extraction for offline analysis.
Then, a higher-level transformer processes these compressed representations to understand longer-term patterns and relationships across the entire video, effectively compressing the temporal dimension while preserving critical information. This global processor receives the local features as input and applies attention across them, enabling the model to recognize complex patterns like cause-effect relationships, recurring motifs, or narrative arcs that span minutes rather than seconds. The global transformer's design often includes specialized mechanisms for handling temporal distance, such as relative position encodings that help the model understand how far apart events occur in time.
This multi-resolution approach also addresses the challenge of variable information density in videos. Action-packed segments might require more detailed analysis, while static scenes need less processing. Advanced implementations dynamically allocate computational resources based on content complexity, spending more computation on informative segments.
For example, in a cooking video, local processing might identify individual actions like "chopping vegetables" or "stirring pot," while global processing would connect these into the complete recipe sequence and understand the relationship between early preparation steps and the final dish. This two-tier approach dramatically reduces computational complexity compared to processing all frames simultaneously while maintaining the ability to capture both fine-grained motions and long-range dependencies. In practical terms, this hierarchical design can reduce memory requirements by 80-90% compared to flat attention across all frames, making it possible to analyze longer videos on standard hardware.
5.3.2 VideoGPT
VideoGPT is a generative model for video built on the transformer architecture, advancing beyond static image generation by incorporating temporal aspects. This model adapts the powerful capabilities of transformer-based language models to the video domain, enabling it to understand and generate complex visual sequences over time. By extending the core principles of text generation to video, VideoGPT demonstrates how the transformer paradigm can be effectively applied across different modalities.
VideoGPT treats video as a sequence of image tokens, converting each frame into a discrete representation that can be processed sequentially. This tokenization process typically involves using a VQ-VAE (Vector Quantized Variational Autoencoder) to compress video frames into a more manageable representation. The resulting tokens form a vocabulary of visual elements that the model can manipulate, similar to how language models work with word tokens. This compression step is crucial because it reduces the dimensionality of the raw video data from millions of pixel values to thousands of discrete tokens, making the subsequent modeling task computationally feasible while preserving essential visual information.
The tokenization process works by training the VQ-VAE to encode frames into a latent space and then quantizing these continuous representations into a finite set of discrete codes from a learned codebook. This codebook effectively becomes the "visual vocabulary" of the model. The transformer component of VideoGPT then learns to predict the next token in this sequence, capturing both short-term frame-to-frame transitions and longer-term visual narratives.
The model learns both spatial and temporal dynamics, enabling frame prediction and video synthesis. This dual capability allows VideoGPT to understand not just what objects appear in a scene (spatial understanding), but how they move and interact over time (temporal understanding). The spatial component handles the composition within individual frames—elements like object appearance, lighting, and scene layout. The temporal component captures motion patterns, object persistence, and causal relationships between events across frames.
This architecture employs masked modeling techniques similar to those used in language models, where certain tokens are hidden during training and the model must predict them. In VideoGPT's case, this might involve masking future frames and training the model to predict them based on past frames, essentially learning to forecast how scenes will evolve.
For example, it can predict how a bouncing ball will continue its trajectory or how water will flow in a stream. This makes VideoGPT useful for applications like video completion, prediction of future frames, and even fully generative video synthesis from text prompts. Beyond these creative applications, the model's understanding of visual dynamics can benefit fields like robotics (predicting physical interactions), autonomous driving (anticipating traffic movements), and virtual reality (generating realistic environmental responses).
Example: Implementing VideoGPT: Video Generation with Transformers
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import numpy as np
import os
from tqdm import tqdm
# 1. VQ-VAE for Video Frame Tokenization
class VQVAE(nn.Module):
def __init__(self, input_dim=3, hidden_dim=128, num_embeddings=1024, embedding_dim=64):
super().__init__()
# Encoder: Convert raw frames to continuous latent space
self.encoder = nn.Sequential(
nn.Conv2d(input_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_dim, embedding_dim, kernel_size=3, stride=1, padding=1)
)
# Vector Quantization: Map continuous vectors to discrete codebook entries
self.codebook = nn.Embedding(num_embeddings, embedding_dim)
self.codebook.weight.data.uniform_(-1.0 / num_embeddings, 1.0 / num_embeddings)
# Decoder: Reconstruct frames from quantized tokens
self.decoder = nn.Sequential(
nn.Conv2d(embedding_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(hidden_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(hidden_dim, input_dim, kernel_size=4, stride=2, padding=1),
nn.Tanh()
)
def encode(self, x):
z = self.encoder(x)
return z
def quantize(self, z):
# Reshape z for quantization
z_flattened = z.permute(0, 2, 3, 1).contiguous().view(-1, z.shape[1])
# Calculate distances to codebook vectors
d = torch.sum(z_flattened**2, dim=1, keepdim=True) + \
torch.sum(self.codebook.weight**2, dim=1) - \
2 * torch.matmul(z_flattened, self.codebook.weight.t())
# Find nearest codebook vector
min_encoding_indices = torch.argmin(d, dim=1)
z_q = self.codebook(min_encoding_indices).view(z.shape[0], z.shape[2], z.shape[3], z.shape[1])
z_q = z_q.permute(0, 3, 1, 2).contiguous()
# Straight-through estimator for gradients
z_q_sg = z + (z_q - z).detach()
return z_q_sg, min_encoding_indices.view(z.shape[0], z.shape[2], z.shape[3])
def decode(self, z_q):
return self.decoder(z_q)
def forward(self, x):
z = self.encode(x)
z_q_sg, indices = self.quantize(z)
x_recon = self.decode(z_q_sg)
return x_recon, z, z_q_sg, indices
# 2. Transformer for Video Prediction
class VideoGPTTransformer(nn.Module):
def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6,
dim_feedforward=2048, max_seq_length=256):
super().__init__()
self.d_model = d_model
# Token embedding: Convert discrete tokens to continuous vectors
self.token_embedding = nn.Embedding(vocab_size, d_model)
# Position encoding: Add information about token position in sequence
self.pos_encoder = nn.Parameter(torch.zeros(1, max_seq_length, d_model))
# Transformer encoder layers
encoder_layers = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True
)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
# Output head: Project to token probabilities
self.output_head = nn.Linear(d_model, vocab_size)
def forward(self, src, src_mask=None):
# src shape: [batch_size, seq_len]
batch_size, seq_len = src.shape
# Embed tokens and add positional encoding
src = self.token_embedding(src) * np.sqrt(self.d_model)
src = src + self.pos_encoder[:, :seq_len, :]
# Pass through transformer
output = self.transformer_encoder(src, src_mask)
# Project to vocabulary space
output = self.output_head(output)
return output
# 3. Dataset for processing video frames
class VideoDataset(Dataset):
def __init__(self, video_dir, frame_size=(64, 64), frames_per_clip=16, transform=None):
self.video_paths = [os.path.join(video_dir, f) for f in os.listdir(video_dir)
if f.endswith(('.mp4', '.avi'))]
self.frame_size = frame_size
self.frames_per_clip = frames_per_clip
self.transform = transform or transforms.Compose([
transforms.Resize(frame_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
def __len__(self):
return len(self.video_paths)
def __getitem__(self, idx):
import cv2
video_path = self.video_paths[idx]
cap = cv2.VideoCapture(video_path)
# Calculate frame sampling
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_indices = np.linspace(0, total_frames-1, self.frames_per_clip, dtype=int)
# Extract frames
frames = []
for frame_idx in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
ret, frame = cap.read()
if ret:
# Convert BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Apply transforms
if self.transform:
frame = self.transform(frame)
frames.append(frame)
cap.release()
# Stack frames along a new dimension
return torch.stack(frames) # Shape: [frames_per_clip, channels, height, width]
# 4. Training Functions
def train_vqvae(vqvae, dataloader, optimizer, epochs=10, device='cuda'):
vqvae.to(device)
for epoch in range(epochs):
total_loss = 0
for batch_idx, frames in enumerate(tqdm(dataloader)):
frames = frames.to(device) # [B, T, C, H, W]
batch_size, time_steps = frames.shape[:2]
# Reshape to process all frames at once
frames_flat = frames.view(-1, *frames.shape[2:]) # [B*T, C, H, W]
optimizer.zero_grad()
# Forward pass through VQ-VAE
x_recon, z, z_q, indices = vqvae(frames_flat)
# Calculate losses
recon_loss = F.mse_loss(x_recon, frames_flat)
# VQ-VAE commitment loss
vq_loss = F.mse_loss(z_q, z.detach())
commitment_loss = F.mse_loss(z, z_q.detach())
# Combined loss
loss = recon_loss + vq_loss + 0.25 * commitment_loss
# Backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
return vqvae
def train_transformer(transformer, vqvae, dataloader, optimizer, epochs=10, device='cuda'):
transformer.to(device)
vqvae.to(device).eval()
for epoch in range(epochs):
total_loss = 0
for batch_idx, frames in enumerate(tqdm(dataloader)):
frames = frames.to(device) # [B, T, C, H, W]
batch_size, time_steps = frames.shape[:2]
# Reshape to process all frames at once
frames_flat = frames.view(-1, *frames.shape[2:]) # [B*T, C, H, W]
# Get token indices from VQ-VAE
with torch.no_grad():
z = vqvae.encode(frames_flat)
_, indices = vqvae.quantize(z)
# Reshape indices back to [batch_size, time_steps, height, width]
indices = indices.view(batch_size, time_steps, *indices.shape[1:])
# Flatten spatial dimensions to get sequence of tokens per frame
# [batch_size, time_steps, height*width]
token_sequences = indices.reshape(batch_size, time_steps, -1)
# For transformer training, we predict next tokens
src = token_sequences[:, :-1].reshape(batch_size, -1) # Input sequence
tgt = token_sequences[:, 1:].reshape(batch_size, -1) # Target sequence
optimizer.zero_grad()
# Create attention mask (optional for training efficiency)
seq_len = src.shape[1]
attn_mask = torch.triu(torch.ones(seq_len, seq_len) * float('-inf'), diagonal=1).to(device)
# Forward pass
output = transformer(src, attn_mask)
# Calculate loss
loss = F.cross_entropy(output.reshape(-1, output.size(-1)), tgt.reshape(-1))
# Backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
return transformer
# 5. Main: Putting it all together
def main():
# Hyperparameters
batch_size = 8
frames_per_clip = 16
frame_size = (64, 64)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Create dataset and dataloader
dataset = VideoDataset(
video_dir="path/to/videos",
frame_size=frame_size,
frames_per_clip=frames_per_clip
)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)
# Step 1: Train VQ-VAE
vqvae = VQVAE(input_dim=3, hidden_dim=128, num_embeddings=1024, embedding_dim=64)
vqvae_optimizer = torch.optim.Adam(vqvae.parameters(), lr=3e-4)
vqvae = train_vqvae(vqvae, dataloader, vqvae_optimizer, epochs=10, device=device)
# Save VQ-VAE model
torch.save(vqvae.state_dict(), "vqvae_model.pth")
# Step 2: Train Transformer
# Number of tokens = codebook size (from VQ-VAE)
vocab_size = 1024 + 1 # +1 for padding token
transformer = VideoGPTTransformer(vocab_size=vocab_size, d_model=512, nhead=8, num_layers=6)
transformer_optimizer = torch.optim.Adam(transformer.parameters(), lr=1e-4)
transformer = train_transformer(transformer, vqvae, dataloader, transformer_optimizer, epochs=20, device=device)
# Save Transformer model
torch.save(transformer.state_dict(), "transformer_model.pth")
# 6. Video Generation Function
def generate_video(vqvae, transformer, seed_frames, num_frames_to_generate=16, device='cuda'):
vqvae.to(device).eval()
transformer.to(device).eval()
# Process seed frames through VQ-VAE to get tokens
with torch.no_grad():
seed_frames = seed_frames.to(device)
z = vqvae.encode(seed_frames)
_, indices = vqvae.quantize(z)
# Flatten spatial dimensions to get sequence of tokens
token_sequence = indices.reshape(1, -1) # [1, time*height*width]
# Generate new frames one by one
generated_tokens = token_sequence.clone()
for _ in range(num_frames_to_generate):
# Predict next tokens
with torch.no_grad():
output = transformer(generated_tokens)
next_token_logits = output[:, -1, :]
next_tokens = torch.argmax(next_token_logits, dim=-1, keepdim=True)
generated_tokens = torch.cat([generated_tokens, next_tokens], dim=1)
# Extract only the newly generated tokens
new_tokens = generated_tokens[:, token_sequence.shape[1]:]
# Reshape tokens to match expected input for VQ-VAE decoder
h, w = indices.shape[1], indices.shape[2]
new_tokens = new_tokens.reshape(-1, h, w) # [num_frames_to_generate, height, width]
# Decode tokens to frames
generated_frames = []
with torch.no_grad():
for tokens in new_tokens:
tokens = tokens.unsqueeze(0) # Add batch dimension
z_q = vqvae.codebook(tokens.view(-1)).view(1, -1, vqvae.codebook.embedding_dim)
z_q = z_q.permute(0, 2, 1).view(1, vqvae.codebook.embedding_dim, h, w)
frame = vqvae.decode(z_q)
generated_frames.append(frame)
# Stack frames along time dimension
return torch.cat(generated_frames, dim=0) # [num_frames_to_generate, C, H, W]
if __name__ == "__main__":
main()
Detailed Explanation of VideoGPT Implementation
This example demonstrates a comprehensive approach to video generation using the VideoGPT architecture. Let's break down the key components:
1. Vector Quantized Variational Autoencoder (VQ-VAE)
The VQ-VAE forms the foundation of VideoGPT by converting raw video frames into discrete tokens:
- Encoder: Compresses video frames into a lower-dimensional continuous latent space using convolutional layers.
- Vector Quantization: Maps these continuous vectors to the nearest vectors in a learned "codebook," effectively discretizing the representation.
- Decoder: Reconstructs the original frames from the quantized representations.
- Straight-through estimator: A technique used during training to allow gradients to flow through the non-differentiable quantization step.
This tokenization process is crucial because it reduces the dimensionality of video data from millions of pixel values to a more manageable set of discrete tokens, making the subsequent modeling task computationally feasible.
2. Transformer Architecture
Once the video frames are tokenized, a transformer model predicts the next tokens in sequence:
- Token Embedding: Converts discrete tokens into continuous vector representations.
- Positional Encoding: Adds information about each token's position in the sequence.
- Transformer Encoder: Processes the token embeddings using self-attention mechanisms to capture dependencies between tokens.
- Output Head: Projects the transformer's output back to token probabilities for prediction.
The transformer architecture allows the model to understand complex spatial-temporal patterns within videos, capturing both short-term frame-to-frame transitions and longer-term visual narratives.
3. Dataset Handling
The custom VideoDataset class handles video processing:
- Extracts frames from video files at regular intervals.
- Applies transformations (resize, normalize) to prepare frames for the model.
- Packages frames into clips of a specified length.
4. Training Process
The training happens in two distinct phases:
- VQ-VAE Training: Optimizes the encoder, codebook, and decoder to effectively compress and reconstruct video frames while building a meaningful discrete representation.
- Transformer Training: After the VQ-VAE is trained, video frames are tokenized and fed to the transformer, which learns to predict future tokens based on past ones.
5. Video Generation
The generation process reverses the training pipeline:
- Seed frames are tokenized through the VQ-VAE encoder and quantizer.
- The transformer autoregressively generates new tokens one by one.
- These tokens are then decoded back into video frames using the VQ-VAE decoder.
Key Technical Insights
- Two-stage Architecture: Separating representation learning (VQ-VAE) from sequence modeling (transformer) makes training more stable and efficient.
- Spatial-Temporal Modeling: The model must capture both spatial relationships within frames and temporal dependencies across frames.
- Autoregressive Generation: Videos are generated one token at a time, with each new token conditioned on all previous tokens.
- Computational Efficiency: Working with discrete tokens rather than raw pixels drastically reduces the computational requirements.
This implementation demonstrates how transformer architectures, originally designed for language modeling, can be effectively adapted to video generation by incorporating appropriate tokenization strategies and handling the additional complexity of temporal data.
5.3.3 Gemini (DeepMind)
Gemini (DeepMind) is a sophisticated multimodal model that seamlessly integrates text, vision, and in some cases video within a unified architecture. Unlike earlier models that treated different data types in isolation, Gemini processes and reasons across multiple input formats simultaneously. This represents a significant advancement over previous approaches where text, images, and video were often processed by separate specialized models and then combined afterward. This unified approach allows Gemini to understand the contextual relationships between different modalities from the ground up rather than trying to merge separately processed information.
The model employs advanced cross-attention mechanisms that enable it to scale effectively across modalities. These attention mechanisms allow the model to identify relationships between elements in different formats—for example, connecting a textual description to relevant parts of an image or linking dialogue to visual events in a video sequence. This architecture enables information to flow bidirectionally between modalities, creating a more holistic understanding. Unlike simple concatenation of different input embeddings, Gemini's cross-attention system allows for dynamic weighting of information across modalities based on context and relevance, similar to how humans naturally shift focus between what they see and hear. This dynamic attention system helps the model determine which aspects of an image might be most relevant to a textual query, or conversely, which parts of a text prompt should inform the understanding of visual content.
Gemini demonstrates impressive reasoning capabilities across a wide range of multimodal inputs, including complex diagrams (such as scientific illustrations or technical schematics), video content (with temporal understanding), and multifaceted prompts that combine several input types. The model can process images at high resolution, enabling it to recognize fine details in photographs, charts, and documents.
For video analysis, Gemini can track objects over time, understand narrative progression, and even anticipate likely future developments based on visual dynamics. This capability is particularly valuable in scenarios requiring detailed visual analysis, such as interpreting medical imagery, understanding engineering diagrams, or analyzing sports footage to extract tactical insights.
This reasoning extends beyond simple recognition to include causal understanding, spatial relationships, and temporal sequences—allowing the model to answer questions like "What will happen next in this physical system?" or "How does this mechanism work?" while referencing visual material. The model's temporal understanding is crucial for tasks that involve processes unfolding over time, such as explaining chemical reactions, analyzing mechanical systems, or tracking changes in biological specimens. This capability resembles human experts' ability to "read" dynamic systems from static diagrams or limited video inputs.
Gemini's multimodal capabilities enable it to solve complex tasks requiring synthesis across modalities, such as interpreting a graph while considering textual context, explaining the steps of a visual process, or identifying inconsistencies between spoken narration and visual content. This integrated approach mirrors human cognition more closely than previous AI systems, as it can form connections between concepts across different representational formats.
This integration facilitates more natural human-AI interaction, allowing users to communicate with the system using whatever combination of text, images, or video best suits their needs, rather than being constrained to a single modality. For example, a user could ask Gemini to analyze a chart, compare it with historical data mentioned in an accompanying text, and explain apparent discrepancies—a task that requires seamless integration of visual and textual information.
Example: Gemini Implementation
import google.generativeai as genai
import PIL.Image
import os
from IPython.display import display, HTML
# Configure API key
genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
# List available models
for m in genai.list_models():
if 'generateContent' in m.supported_generation_methods:
print(m.name)
# Select Gemini Pro Vision model
model = genai.GenerativeModel('gemini-pro-vision')
# Function to analyze an image with text prompt
def analyze_image(image_path, prompt):
img = PIL.Image.open(image_path)
response = model.generate_content([prompt, img])
return response.text
# Function for multimodal reasoning with multiple images
def compare_images(image_path1, image_path2, prompt):
img1 = PIL.Image.open(image_path1)
img2 = PIL.Image.open(image_path2)
response = model.generate_content([prompt, img1, img2])
return response.text
# Example usage: Image analysis
image_analysis = analyze_image("chart.jpg",
"Analyze this chart in detail. What trends do you observe?")
print(image_analysis)
# Example usage: Image comparison
comparison = compare_images("design_v1.jpg", "design_v2.jpg",
"Compare these two design versions and explain the key differences.")
print(comparison)
# Example: Complex reasoning with image and specific instructions
reasoning = analyze_image("scientific_diagram.jpg",
"Explain how this biological process works. Focus on:
1. The starting materials
2. The transformation steps
3. The end products
4. The energy changes involved")
print(reasoning)
# Example: Video frame analysis
def analyze_video_frames(frame_paths, prompt):
frames = [PIL.Image.open(path) for path in frame_paths]
response = model.generate_content([prompt] + frames)
return response.text
frame_paths = ["video_frame1.jpg", "video_frame2.jpg", "video_frame3.jpg"]
video_analysis = analyze_video_frames(frame_paths,
"Analyze the motion sequence shown in these frames. What's happening?")
print(video_analysis)
# Safety settings example (optional)
safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_ONLY_HIGH"
}
]
model_with_safety = genai.GenerativeModel(
model_name='gemini-pro-vision',
safety_settings=safety_settings
)
Understanding the Gemini Implementation
The code example above demonstrates how to work with Google's Gemini multimodal model, providing a practical framework for integrating vision and language understanding. Let's explore the key components and capabilities:
API Configuration and Model Selection
The implementation begins by importing the necessary libraries and configuring the API with an authentication key. The code then lists available models with content generation capabilities before selecting the Gemini Pro Vision model, which is specifically designed for multimodal tasks combining text and images.
Core Functionality
The implementation provides several functions that showcase Gemini's multimodal capabilities:
- Single Image Analysis: The
analyze_image()function accepts an image path and a text prompt, then returns Gemini's interpretation of the image in the context of the prompt. This enables tasks like chart analysis, object identification, or scene description. - Comparative Image Analysis: With
compare_images(), the model can reason about relationships between multiple images, identifying similarities, differences, and patterns across visual content. This is useful for before/after comparisons, design iterations, or tracking changes. - Video Frame Analysis: Though Gemini doesn't process video directly in this implementation, the
analyze_video_frames()function demonstrates how to analyze temporal sequences by feeding multiple frames with a contextual prompt. This allows for basic motion analysis and event understanding across time.
Prompt Engineering for Multimodal Tasks
The example showcases several prompt structures that enable different types of visual reasoning:
- Open-ended analysis: "Analyze this chart in detail. What trends do you observe?" allows the model to identify and describe patterns with minimal constraints.
- Comparative analysis: "Compare these two design versions and explain the key differences" directs the model to focus specifically on contrasting visual elements.
- Structured reasoning: The scientific diagram prompt uses a numbered list to guide the model through a systematic analysis process, ensuring comprehensive coverage of specific aspects.
- Temporal understanding: "Analyze the motion sequence shown in these frames" encourages the model to consider relationships between images as representing a continuous process rather than isolated visuals.
Safety Considerations
The implementation includes optional safety settings that can be configured to control the model's outputs according to different harm categories and thresholds. This demonstrates how to implement responsible AI practices when deploying multimodal systems that might encounter or generate sensitive content.
Technical Significance
What makes this implementation particularly powerful is its simplicity relative to the complexity of the underlying model. The Gemini architecture internally handles the complex cross-attention mechanisms that align visual and textual information, allowing developers to interact with it through a straightforward API.
Unlike previous approaches that required separate models for vision and language tasks, Gemini's unified architecture enables it to process both modalities jointly, capturing the interactions between them. This is evident in how a single function call can pass both text and images to the model and receive coherent, contextually relevant responses.
Practical Applications
This implementation enables numerous real-world applications:
- Data visualization interpretation: Automatically generating insights from charts, graphs, and other visual data representations.
- Document understanding: Analyzing documents that combine text and images, such as technical manuals, academic papers, or illustrated guides.
- Educational content analysis: Processing instructional materials that use diagrams and text explanations to convey complex concepts.
- Design feedback: Providing structured analysis of visual designs, identifying issues and suggesting improvements.
- Medical image preliminary assessment: Assisting healthcare professionals by providing initial observations on medical imagery alongside clinical notes.
The example demonstrates how Gemini bridges the gap between computer vision and natural language processing, offering an integrated approach to understanding the visual world through the lens of language and vice versa.
5.3.4 Kosmos-2 (Microsoft)
Focuses on grounding language in vision, which means creating explicit connections between language descriptions and specific visual elements. This technique enables the model to understand not just what objects are in an image, but precisely where they are located and how they relate to linguistic references. The model essentially creates a detailed spatial map of the image, connecting language tokens directly to pixel regions. This grounding capability represents a fundamental shift in how AI processes visual information, moving from general scene understanding to precise object localization and reference resolution—similar to how humans point at objects while describing them. Just as a person might say "look at that red bird on the branch" while pointing, Kosmos-2 can conceptually "point" to objects it describes.
Can link words to objects in an image or video frame, enabling tasks like "point to the cat in the video." This capability represents a significant advancement over earlier models that could only describe images generally but couldn't identify specific regions or elements when prompted. For example, when asked "What is the person on the left wearing?", Kosmos-2 can both understand the spatial reference ("on the left") and ground its response to the specific person being referenced. It can generate bounding boxes or segmentation masks that highlight exactly which pixels in the image correspond to "the person on the left" before answering about their clothing. This requires sophisticated visual reasoning that combines object detection, spatial awareness, and natural language understanding in a unified framework—a computational challenge that previous generations of models struggled to address. The model must simultaneously parse language, recognize objects, understand spatial relationships, and maintain the connections between them all.
A step toward cross-modal grounding, where models tie abstract descriptions to concrete visual elements. This connection between language and vision mimics how humans naturally communicate about visual information, allowing for more precise visual reasoning, improved human-AI interaction, and the foundation for embodied AI systems that need to understand references to objects in their environment. Rather than treating language and vision as separate domains that occasionally interact, Kosmos-2 builds a shared representational space where concepts from either modality can be mapped directly to each other. The grounding capability is especially valuable for applications like visual question answering, image editing based on natural language instructions, and assistive technologies for the visually impaired. For instance, a visually impaired user could ask "Is there a cup on the table?" and receive not just a yes/no answer, but information about where exactly the cup is located relative to other objects.
By establishing direct links between words and visual regions, Kosmos-2 creates a foundation for more sophisticated reasoning tasks that require understanding both the semantics of language and the spatial configuration of visual scenes—capabilities that are essential for robots navigating physical environments, AR/VR systems responding to natural language commands about visible objects, or accessibility tools that help visually impaired users understand their surroundings through verbal descriptions. This grounding mechanism also enables multi-turn interactions about specific parts of an image, where a user might ask "What's in the corner?" followed by "What color is it?" and the model correctly maintains context about which object is being discussed. The alignment between language and vision provides a crucial building block for AI systems that must operate in the physical world, where understanding references to objects and their relationships is fundamental to meaningful interaction.
Example: Kosmos-2 Implementation
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import requests
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# Load Kosmos-2 model and processor
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
# Function to get image from URL
def get_image(url):
image = Image.open(requests.get(url, stream=True).raw)
return image
# Function to process image and generate caption with bounding boxes
def analyze_with_grounding(image, prompt="<grounding>Describe this image in detail:"):
# Process the image and text
inputs = processor(text=prompt, images=image, return_tensors="pt")
# Generate output from model
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
num_beams=5,
early_stopping=True
)
# Decode the generated text with grounding tokens
generated_text = processor.batch_decode(outputs, skip_special_tokens=False)[0]
# Extract bounding boxes from the generated output
phrase_bboxes = []
for phrase, bbox in processor.parse_bbox(generated_text):
if bbox is not None:
phrase_bboxes.append((phrase, bbox))
return generated_text, phrase_bboxes
# Function to visualize the image with bounding boxes
def visualize_with_bboxes(image, phrase_bboxes):
plt.figure(figsize=(16, 10))
plt.imshow(image)
ax = plt.gca()
# Add bounding boxes with labels
for phrase, bbox in phrase_bboxes:
x, y, width, height = bbox
rect = patches.Rectangle(
(x * image.width, y * image.height),
width * image.width,
height * image.height,
linewidth=2,
edgecolor='r',
facecolor='none'
)
ax.add_patch(rect)
plt.text(
x * image.width,
y * image.height - 5,
phrase,
color='white',
backgroundcolor='red',
fontsize=10
)
plt.axis('off')
plt.tight_layout()
plt.show()
# Function for comparing objects in an image
def compare_objects(image, prompt="<grounding>Compare the objects in this image:"):
generated_text, phrase_bboxes = analyze_with_grounding(image, prompt)
print("Generated Text:", generated_text)
visualize_with_bboxes(image, phrase_bboxes)
return generated_text, phrase_bboxes
# Function for referring expression comprehension
def find_specific_object(image, object_description):
prompt = f"<grounding>Point to the {object_description} in this image."
generated_text, phrase_bboxes = analyze_with_grounding(image, prompt)
print(f"Looking for: {object_description}")
print("Generated Text:", generated_text)
visualize_with_bboxes(image, phrase_bboxes)
return generated_text, phrase_bboxes
# Example usage
image_url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/living_room.jpg"
image = get_image(image_url)
# Basic image description with grounding
description, bboxes = analyze_with_grounding(image)
print("Description with grounding:")
print(description)
visualize_with_bboxes(image, bboxes)
# Find a specific object
find_specific_object(image, "red couch")
# Compare objects
compare_objects(image, "<grounding>Compare the furniture items in this image.")
# Spatial reasoning example
spatial_reasoning = find_specific_object(image, "lamp next to the couch")
Understanding the Kosmos-2 Implementation
The code example above demonstrates how to work with Microsoft's Kosmos-2 multimodal model, showcasing its unique capability for visual grounding. Let's break down the key components and capabilities:
Setup and Initialization
The implementation begins by importing the necessary libraries and initializing the Kosmos-2 model and processor from the Hugging Face Transformers library. Kosmos-2 is accessed through the AutoModelForVision2Seq class, which handles models that can process both vision and language.
Core Grounding Functionality
The central function analyze_with_grounding() demonstrates Kosmos-2's key innovation: the ability to connect language descriptions with specific visual elements through grounding. The function:
Processes an image along with a prompt that includes the special `` token to activate the model's grounding capabilities
- Generates a descriptive response about the image
- Extracts bounding box coordinates for objects that the model has identified and mentioned
- Returns both the generated text and a list of phrase-bounding box pairs
Visual Grounding in Action
The visualize_with_bboxes() function provides a visualization capability that overlays the model's detected objects on the original image. This visual representation shows how Kosmos-2 connects its language understanding with precise spatial locations in the image, effectively demonstrating the model's ability to "point" at objects it's describing.
Advanced Visual Reasoning Capabilities
The implementation includes specialized functions that showcase different aspects of Kosmos-2's visual reasoning abilities:
- Object Comparison: The
compare_objects()function prompts the model to identify and compare multiple objects in an image, highlighting each with bounding boxes. This demonstrates the model's ability to reason about relationships between different visual elements. - Referring Expression Comprehension: With
find_specific_object(), the model locates specific objects based on natural language descriptions. This capability is essential for tasks requiring precise object localization based on verbal instructions. - Spatial Reasoning: The example shows how Kosmos-2 can understand spatial relationships between objects (e.g., "lamp next to the couch"), combining object recognition with positional awareness.
Prompt Engineering for Grounding
The example highlights the importance of the `` token in prompts, which serves as a special instruction to the model to activate its visual grounding capabilities. Different prompting strategies demonstrate various aspects of visual reasoning:
- "Describe this image in detail" triggers comprehensive scene understanding with object localization
- "Point to the [object]" focuses the model on locating a specific item
- "Compare the objects" encourages the model to identify multiple entities and reason about their similarities and differences
Technical Significance
What makes Kosmos-2 particularly innovative is its ability to create explicit connections between natural language descriptions and specific regions in an image. Unlike earlier multimodal models that could generally describe an image but couldn't pinpoint specific objects, Kosmos-2's grounding mechanism enables:
- Precise object localization in response to natural language queries
- Fine-grained understanding of spatial relationships between objects
- The ability to answer questions about specific parts of an image
- More natural human-AI interaction by mimicking how humans point while describing
Practical Applications
This implementation of Kosmos-2 enables numerous real-world applications:
- Assistive technology: Helping visually impaired users understand their surroundings by describing specific objects and their locations
- Visual search: Finding objects in images based on natural language descriptions
- Human-robot interaction: Enabling robots to understand references to objects in their environment
- Visual question answering: Providing detailed answers about specific elements in an image
- Educational tools: Creating interactive learning experiences that connect visual concepts with language
Kosmos-2 represents an important step toward AI systems that can perceive and reason about the visual world in ways that more closely resemble human understanding, bridging the gap between seeing and communicating about what is seen.
Example: Extracting Features from Video with Hugging Face
We can’t yet run full Gemini or Kosmos in open-source, but we can use pretrained models like VideoMAE for video embeddings.
from transformers import VideoMAEFeatureExtractor, VideoMAEModel
import torch
import av # pip install av
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os
import time
def load_video_mae_model():
"""Load the pretrained VideoMAE model and feature extractor"""
print("Loading VideoMAE model...")
feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
return feature_extractor, model
def extract_frames(video_path, num_frames=8, sample_rate=30):
"""Extract frames from a video file at a specific sample rate
Args:
video_path: Path to the video file
num_frames: Maximum number of frames to extract
sample_rate: Extract every nth frame
Returns:
List of frames as numpy arrays in RGB format
"""
print(f"Extracting frames from {video_path}...")
if not os.path.exists(video_path):
raise FileNotFoundError(f"Video file not found: {video_path}")
container = av.open(video_path)
frames = []
# Get video info
video_stream = container.streams.video[0]
fps = video_stream.average_rate
duration = container.duration / 1000000 # in seconds
total_frames = video_stream.frames
print(f"Video info: {fps} fps, {duration:.2f}s duration, {total_frames} total frames")
start_time = time.time()
for i, frame in enumerate(container.decode(video=0)):
if i % sample_rate == 0:
frames.append(frame.to_ndarray(format="rgb24"))
print(f"Extracted frame {len(frames)}/{num_frames} (video position: {i})")
if len(frames) == num_frames:
break
process_time = time.time() - start_time
print(f"Frame extraction complete. Extracted {len(frames)} frames in {process_time:.2f}s")
return frames
def get_video_embeddings(feature_extractor, model, frames):
"""Process frames and extract embeddings using VideoMAE
Args:
feature_extractor: VideoMAE feature extractor
model: VideoMAE model
frames: List of video frames as numpy arrays
Returns:
Video embeddings tensor and raw model outputs
"""
if len(frames) == 0:
raise ValueError("No frames were extracted from the video")
print(f"Processing {len(frames)} frames with VideoMAE...")
# Preprocess frames
inputs = feature_extractor(frames, return_tensors="pt")
# Extract embeddings
with torch.no_grad():
outputs = model(**inputs)
video_embeddings = outputs.last_hidden_state
return video_embeddings, outputs
def visualize_frames_and_embeddings(frames, embeddings):
"""Visualize extracted frames and a 2D PCA projection of their embeddings"""
# Visualize frames
num_frames = len(frames)
fig, axes = plt.subplots(1, num_frames, figsize=(16, 4))
for i, (frame, ax) in enumerate(zip(frames, axes)):
ax.imshow(frame)
ax.set_title(f"Frame {i}")
ax.axis('off')
plt.tight_layout()
plt.savefig("video_frames.png")
plt.show()
# Visualize embedding patterns (simple 2D visualization)
# Take mean over tokens dimension to get per-frame representations
frame_embeddings = embeddings.mean(dim=1).squeeze(0)
# PCA-like dimensionality reduction (simplified)
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
reduced_embeddings = pca.fit_transform(frame_embeddings.numpy())
plt.figure(figsize=(8, 6))
plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1])
# Add frame numbers
for i, (x, y) in enumerate(reduced_embeddings):
plt.annotate(str(i), (x, y), fontsize=12)
plt.title("2D projection of frame embeddings")
plt.xlabel("Component 1")
plt.ylabel("Component 2")
plt.grid(True, linestyle='--', alpha=0.7)
plt.savefig("embedding_visualization.png")
plt.show()
def compute_frame_similarity(embeddings):
"""Compute cosine similarity between frame embeddings"""
# Mean over token dimension to get per-frame embeddings
frame_embeddings = embeddings.mean(dim=1).squeeze(0)
# Normalize embeddings
norm = frame_embeddings.norm(dim=1, keepdim=True)
normalized_embeddings = frame_embeddings / norm
# Compute similarity matrix
similarity = torch.mm(normalized_embeddings, normalized_embeddings.t())
# Visualize similarity matrix
plt.figure(figsize=(8, 6))
plt.imshow(similarity.numpy(), cmap='viridis')
plt.colorbar(label='Cosine Similarity')
plt.title("Frame-to-Frame Similarity")
plt.xlabel("Frame Index")
plt.ylabel("Frame Index")
plt.savefig("frame_similarity.png")
plt.show()
return similarity
def detect_scene_changes(similarity_matrix, threshold=0.8):
"""Simple scene change detection based on frame similarity"""
# Check if adjacent frames are below similarity threshold
scene_changes = []
sim_np = similarity_matrix.numpy()
for i in range(len(sim_np) - 1):
if sim_np[i, i+1] < threshold:
scene_changes.append(i+1)
print(f"Detected {len(scene_changes)} potential scene changes at frames: {scene_changes}")
return scene_changes
def main():
# Load model
feature_extractor, model = load_video_mae_model()
# Process video
video_path = "sample_video.mp4"
frames = extract_frames(video_path, num_frames=8, sample_rate=30)
# Get embeddings
video_embeddings, outputs = get_video_embeddings(feature_extractor, model, frames)
print("Video embeddings shape:", video_embeddings.shape) # [batch, frames, hidden_dim]
# Visualize frames and embeddings
visualize_frames_and_embeddings(frames, video_embeddings)
# Compute and visualize frame similarity
similarity = compute_frame_similarity(video_embeddings)
# Detect scene changes
scene_changes = detect_scene_changes(similarity, threshold=0.8)
print("Processing complete!")
if __name__ == "__main__":
main()
The example above demonstrates a comprehensive approach to working with videos in machine learning contexts using the VideoMAE (Video Masked Autoencoder) model. VideoMAE is a self-supervised learning framework for video understanding that works by reconstructing masked portions of video frames. Let's break down the key components:
Video Frame Extraction: The code uses the PyAV library to efficiently decode and extract frames from video files at specified intervals. This is crucial for video processing since working with every frame would be computationally expensive and often redundant, as adjacent frames typically contain similar information.
Feature Extraction with VideoMAE: The extracted frames are processed through VideoMAE, which transforms the raw pixel data into high-dimensional feature vectors (embeddings). These embeddings capture semantic information about objects, actions, and scenes present in the video.
Visualization Components: The code includes several visualization functions that help understand both the raw video content (displaying extracted frames) and the encoded representations (embedding visualizations). This is valuable for debugging and gaining insights into how the model "sees" the video.
Frame Similarity Analysis: By computing cosine similarity between frame embeddings, the code can identify how similar or different consecutive frames are. This has practical applications in scene boundary detection, content summarization, and keyframe extraction.
Scene Change Detection: A simple threshold-based approach is implemented to detect potential scene changes, which could be useful for video indexing, summarization, or creating chapter markers.
The code represents a foundation for more complex video understanding tasks like action recognition, video captioning, or video question answering. These capabilities are essential for applications ranging from content moderation and video search to assistive technologies for the visually impaired.
When working with the VideoMAE model, it's important to understand that:
- The model's input preprocessing is specific and requires frames to be in a particular format and dimension.
- The output embeddings are hierarchical and capture different levels of temporal and spatial information.
- The token dimension in the output shape represents the spatial tokens that the image is divided into.
- For downstream tasks, you would typically need to apply additional processing or fine-tuning to adapt these generic embeddings for specific purposes.
This code example provides a solid starting point for exploring multimodal capabilities that bridge the gap between computer vision and natural language processing, which is increasingly important as AI systems need to understand the world in ways that more closely resemble human perception.
5.3.5 Cross-Modal Reasoning
Cross-modal reasoning goes beyond processing modalities in isolation. It's about integration - the ability to synthesize and analyze information across different perceptual channels simultaneously. This represents a significant advancement over systems that can only process one type of input at a time, as it mirrors how humans naturally perceive and understand the world around them.
Unlike traditional AI systems that handle each input type separately, cross-modal models create a unified understanding by establishing connections between different types of information, enabling more comprehensive and contextual analysis. This integration happens at a deep representational level, where the model learns to map concepts across modalities into a shared semantic space.
For example, when a cross-modal system processes both an image of a dog and the word "dog," it doesn't treat these as separate, unrelated inputs. Instead, it recognizes they refer to the same concept despite coming through different perceptual channels. This ability to form these cross-modal associations is fundamental to human cognition and represents a crucial step toward more human-like AI understanding.
The technical implementation of cross-modal reasoning often involves complex neural architectures with shared embedding spaces, cross-attention mechanisms, and fusion techniques that preserve the unique characteristics of each modality while enabling information to flow between them. These systems must learn not just to process each modality effectively but to identify meaningful correlations between them, distinguishing relevant connections from coincidental ones.
Audio + Video:
Lip-reading and voice alignment, where models can match spoken words with mouth movements to improve speech recognition in noisy environments or for hearing-impaired users. This integration allows for more robust communication understanding. The system analyzes both the visual cues of lip movements and the acoustic properties of speech, compensating for deficiencies in either modality.
When processing lip movements, these systems track facial landmarks and mouth shapes that correspond to specific phonemes (speech sounds). Meanwhile, the audio component analyzes spectral and temporal features of the speech signal. By combining these streams of information, the system can disambiguate similar-sounding phonemes that have distinct visual representations (like "ba" vs "fa") or clarify unclear audio by leveraging the visual channel.
Advanced models employ attention mechanisms that dynamically weight the importance of visual versus audio inputs depending on their reliability. For instance, when ambient noise increases, the system automatically places greater emphasis on visual information. Conversely, in low-light conditions where visual data is less reliable, the audio channel receives higher priority.
This is particularly valuable in crowded settings where background noise might otherwise make speech recognition impossible, or in assistive technologies for people with hearing impairments who rely partly on visual cues for communication. In teleconferencing applications, this technology helps maintain clear communication even with unstable internet connections by reconstructing parts of the message from the available modality.
Example: Cross-Modal Reasoning: Audio + Video Integration
import torch
import torchaudio
import torch.nn as nn
import torch.nn.functional as F
import av
import numpy as np
import matplotlib.pyplot as plt
from transformers import Wav2Vec2Processor, Wav2Vec2Model
from transformers import VideoMAEFeatureExtractor, VideoMAEModel
from sklearn.metrics.pairwise import cosine_similarity
from PIL import Image
import librosa
import librosa.display
class AudioVideoSyncModel(nn.Module):
"""
A model for audio-video synchronization and cross-modal reasoning
"""
def __init__(self, audio_dim=768, video_dim=768, joint_dim=512):
super().__init__()
self.audio_projection = nn.Linear(audio_dim, joint_dim)
self.video_projection = nn.Linear(video_dim, joint_dim)
self.cross_attention = nn.MultiheadAttention(
embed_dim=joint_dim,
num_heads=8,
batch_first=True
)
self.classifier = nn.Sequential(
nn.Linear(joint_dim, 256),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(256, 1),
nn.Sigmoid()
)
def forward(self, audio_features, video_features):
"""
Process audio and video features and compute synchronization score
Args:
audio_features: Tensor of shape [batch_size, seq_len_audio, audio_dim]
video_features: Tensor of shape [batch_size, seq_len_video, video_dim]
Returns:
sync_score: Synchronization probability between 0-1
joint_features: Cross-modal features after attention
"""
# Project to common space
audio_proj = self.audio_projection(audio_features)
video_proj = self.video_projection(video_features)
# Apply cross-attention from video to audio
joint_features, _ = self.cross_attention(
query=video_proj,
key=audio_proj,
value=audio_proj
)
# Get global representation by mean pooling
global_joint = torch.mean(joint_features, dim=1)
# Predict synchronization score
sync_score = self.classifier(global_joint)
return sync_score, joint_features
def extract_video_frames(video_path, sample_rate=5):
"""
Extract frames from a video at regular intervals
Args:
video_path: Path to video file
sample_rate: Sample every nth frame
Returns:
List of frames as numpy arrays in RGB format
"""
frames = []
try:
container = av.open(video_path)
stream = container.streams.video[0]
total_frames = stream.frames
fps = float(stream.average_rate)
print(f"Video: {total_frames} frames, {fps} fps")
for i, frame in enumerate(container.decode(video=0)):
if i % sample_rate == 0:
# Convert to RGB numpy array
img = frame.to_ndarray(format='rgb24')
frames.append(img)
print(f"Extracted {len(frames)} frames")
container.close()
except Exception as e:
print(f"Error extracting video frames: {e}")
return frames
def extract_audio_from_video(video_path, target_sr=16000):
"""
Extract audio from a video file
Args:
video_path: Path to video file
target_sr: Target sampling rate
Returns:
Audio waveform and sample rate
"""
try:
container = av.open(video_path)
audio_stream = container.streams.audio[0]
# Initialize an empty numpy array to store audio samples
audio_data = []
# Decode audio
for frame in container.decode(audio=0):
# Convert PyAV AudioFrame to numpy array
frame_data = frame.to_ndarray()
audio_data.append(frame_data)
# Concatenate audio frames
if audio_data:
audio_array = np.concatenate(audio_data)
# Convert to mono if stereo
if len(audio_array.shape) > 1 and audio_array.shape[1] > 1:
audio_array = np.mean(audio_array, axis=1)
# Resample if needed
original_sr = audio_stream.rate
if original_sr != target_sr:
audio_resampled = librosa.resample(
audio_array,
orig_sr=original_sr,
target_sr=target_sr
)
return audio_resampled, target_sr
return audio_array, original_sr
else:
raise ValueError("No audio frames found")
except Exception as e:
print(f"Error extracting audio: {e}")
return None, None
def process_video(video_path, video_model, video_processor, sample_rate=5):
"""
Extract and process video frames
Args:
video_path: Path to video file
video_model: VideoMAE model
video_processor: VideoMAE feature extractor
sample_rate: Sample every nth frame
Returns:
Video features tensor
"""
# Extract frames
frames = extract_video_frames(video_path, sample_rate)
if not frames:
raise ValueError("No frames were extracted")
# Process frames with VideoMAE
inputs = video_processor(frames, return_tensors="pt")
with torch.no_grad():
outputs = video_model(**inputs)
video_features = outputs.last_hidden_state
return video_features, frames
def process_audio(audio_array, sr, audio_model, audio_processor):
"""
Process audio with Wav2Vec2
Args:
audio_array: Audio samples as numpy array
sr: Sample rate
audio_model: Wav2Vec2 model
audio_processor: Wav2Vec2 processor
Returns:
Audio features tensor
"""
# Prepare audio for Wav2Vec2
inputs = audio_processor(
audio_array,
sampling_rate=sr,
return_tensors="pt"
)
with torch.no_grad():
outputs = audio_model(**inputs)
audio_features = outputs.last_hidden_state
return audio_features
def detect_audiovisual_sync(sync_scores, threshold=0.5):
"""
Analyze synchronization scores to detect in-sync vs out-of-sync segments
Args:
sync_scores: List of synchronization scores
threshold: Threshold for considering audio-video in sync
Returns:
List of in-sync and out-of-sync segments
"""
segments = []
current_segment = {"start": 0, "status": "sync" if sync_scores[0] >= threshold else "out-of-sync"}
for i in range(1, len(sync_scores)):
current_status = "sync" if sync_scores[i] >= threshold else "out-of-sync"
previous_status = "sync" if sync_scores[i-1] >= threshold else "out-of-sync"
if current_status != previous_status:
# End the previous segment
current_segment["end"] = i - 1
segments.append(current_segment)
# Start a new segment
current_segment = {"start": i, "status": current_status}
# Add the final segment
current_segment["end"] = len(sync_scores) - 1
segments.append(current_segment)
return segments
def visualize_sync_analysis(frames, audio_waveform, sr, sync_scores, segments):
"""
Visualize audio, video frames, and synchronization analysis
Args:
frames: List of video frames
audio_waveform: Audio samples
sr: Audio sample rate
sync_scores: Synchronization scores
segments: Detected sync/out-of-sync segments
"""
fig, axes = plt.subplots(3, 1, figsize=(15, 10), gridspec_kw={'height_ratios': [1, 1, 2]})
# Plot synchronization scores
axes[0].plot(sync_scores)
axes[0].set_ylim(0, 1)
axes[0].set_ylabel('Sync Score')
axes[0].set_xlabel('Frame')
axes[0].axhline(y=0.5, color='r', linestyle='--')
# Highlight sync/out-of-sync segments
for segment in segments:
color = 'green' if segment['status'] == 'sync' else 'red'
axes[0].axvspan(segment['start'], segment['end'], alpha=0.2, color=color)
# Plot audio waveform
librosa.display.waveshow(audio_waveform, sr=sr, ax=axes[1])
axes[1].set_ylabel('Amplitude')
# Display frames at key points
n_frames = min(8, len(frames))
indices = np.linspace(0, len(frames)-1, n_frames, dtype=int)
for i, idx in enumerate(indices):
ax = plt.subplot(3, n_frames, i + 2*n_frames + 1)
ax.imshow(frames[idx])
ax.set_title(f"Frame {idx}")
ax.axis('off')
plt.tight_layout()
plt.savefig('av_sync_analysis.png')
plt.show()
def demonstrate_lip_reading(sync_model, audio_features, video_features, frames):
"""
Demonstrate lip-reading by finding the most relevant audio for each video frame
using the cross-attention mechanism
Args:
sync_model: Trained AudioVideoSyncModel
audio_features: Audio features tensor
video_features: Video features tensor
frames: List of video frames
Returns:
Attention weights showing audio-visual connections
"""
# Project features to common space
audio_proj = sync_model.audio_projection(audio_features)
video_proj = sync_model.video_projection(video_features)
# Compute raw attention scores
attn_weights = torch.matmul(video_proj, audio_proj.transpose(-2, -1)) / np.sqrt(audio_proj.size(-1))
# Convert to probabilities
attn_probs = F.softmax(attn_weights, dim=-1)
# Visualize attention for selected frames
n_frames = min(4, len(frames))
indices = np.linspace(0, len(frames)-1, n_frames, dtype=int)
fig, axes = plt.subplots(2, n_frames, figsize=(15, 6))
for i, idx in enumerate(indices):
# Show the frame
axes[0, i].imshow(frames[idx])
axes[0, i].set_title(f"Frame {idx}")
axes[0, i].axis('off')
# Show attention weights (which audio segments this frame attends to)
if idx < attn_probs.shape[1]: # Ensure index is valid
axes[1, i].plot(attn_probs[0, idx].detach().numpy())
axes[1, i].set_title("Audio Attention")
axes[1, i].set_xlabel("Audio Frames")
axes[1, i].set_ylabel("Attention Weight")
plt.tight_layout()
plt.savefig('lip_reading_attention.png')
plt.show()
return attn_probs
def main():
# Initialize models
print("Loading models...")
# Audio model (Wav2Vec2)
audio_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
audio_model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
# Video model (VideoMAE)
video_processor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
video_model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
# Cross-modal synchronization model
sync_model = AudioVideoSyncModel(
audio_dim=768, # Wav2Vec2 feature dimension
video_dim=768, # VideoMAE feature dimension
joint_dim=512 # Joint embedding dimension
)
# Process video with speaking person
video_path = "speaking_person.mp4"
print(f"Processing video: {video_path}")
# Extract audio from video
audio_array, sr = extract_audio_from_video(video_path)
if audio_array is None:
print("Failed to extract audio")
return
print(f"Audio: {len(audio_array)} samples, {sr} Hz")
# Process video frames
video_features, frames = process_video(
video_path,
video_model,
video_processor,
sample_rate=5
)
# Process audio
audio_features = process_audio(
audio_array,
sr,
audio_model,
audio_processor
)
print("Audio features shape:", audio_features.shape)
print("Video features shape:", video_features.shape)
# Simulate training the sync model (in practice, this requires proper training)
# Here we're just demonstrating the forward pass
sync_scores = []
step_size = max(1, audio_features.shape[1] // video_features.shape[1])
for i in range(video_features.shape[1]):
# Get corresponding audio chunk
start_idx = i * step_size
end_idx = min((i + 1) * step_size, audio_features.shape[1])
audio_chunk = audio_features[:, start_idx:end_idx, :]
video_frame_feat = video_features[:, i:i+1, :]
# Mean pool audio chunk
audio_chunk_pooled = torch.mean(audio_chunk, dim=1, keepdim=True)
# Get sync score
score, _ = sync_model(audio_chunk_pooled, video_frame_feat)
sync_scores.append(score.item())
# Analyze synchronization
segments = detect_audiovisual_sync(sync_scores)
print("Detected segments:")
for segment in segments:
print(f"Frames {segment['start']}-{segment['end']}: {segment['status']}")
# Visualize results
visualize_sync_analysis(frames, audio_array, sr, sync_scores, segments)
# Demonstrate lip reading capabilities
print("Generating lip reading visualization...")
attn_weights = demonstrate_lip_reading(sync_model, audio_features, video_features, frames)
print("Analysis complete!")
if __name__ == "__main__":
main()
This code example demonstrates a comprehensive approach to cross-modal reasoning with audio and video, focusing on lip-reading and speech-video synchronization analysis. Let's break down the key components:
The AudioVideoSyncModel class implements a neural network architecture that processes both audio and video features and learns to align them in a shared representation space. It uses several important mechanisms:
- Modal-specific projection layers that map audio and video features to a common semantic space
- Cross-attention mechanisms that allow the model to determine which parts of the audio correspond to which visual frames
- A classification head that predicts whether audio and video are synchronized
The extract_video_frames function extracts frames from a video at regular intervals using PyAV, which provides a Pythonic binding to the FFmpeg libraries. This sampling approach is essential for efficiency since processing every frame would be computationally expensive and often redundant for semantic understanding.
Similarly, extract_audio_from_video extracts the audio track from a video file and processes it into a format suitable for deep learning models, including converting to a consistent sampling rate and handling multi-channel audio.
The process_video and process_audio functions use pretrained models from the Transformers library to convert raw video frames and audio signals into high-dimensional feature representations:
- VideoMAE (Video Masked Autoencoder) processes video frames, extracting features that capture objects, actions, and visual context
- Wav2Vec2 processes audio, capturing phonetic and linguistic information
The detect_audiovisual_sync function analyzes the synchronization scores to identify segments where audio and video are well-aligned versus segments where they might be out of sync. This is valuable for applications like automatic correction of audio-video synchronization issues in recorded content.
The visualize_sync_analysis function creates a comprehensive visualization showing:
- The synchronization scores over time
- Color-coded segments indicating in-sync and out-of-sync portions
- The audio waveform
- Key video frames from throughout the sequence
The demonstrate_lip_reading function shows how the cross-attention mechanism effectively implements a form of lip reading by connecting mouth movements in video frames with corresponding audio segments. It visualizes the attention weights, showing which parts of the audio each video frame is most strongly associated with.
In the main function, we see the entire pipeline in action:
- Models are loaded and initialized
- Video and audio are extracted and processed
- The synchronization model is applied to analyze the alignment between modalities
- Results are visualized for interpretation
This implementation has numerous practical applications:
- Assistive technology for hearing-impaired users to enhance speech understanding with visual cues
- Video production tools that automatically detect and correct audio-video synchronization issues
- Enhanced speech recognition in noisy environments by leveraging visual information
- Security applications for detecting manipulated content where audio and video don't naturally align
- Educational tools that ensure properly synchronized content for optimal learning experiences
The example represents a foundation that could be extended with more sophisticated training procedures and architectural improvements. In a production environment, this system would require proper training data consisting of paired audio-video examples with both synchronized and deliberately misaligned samples.
Text + Image
Answering questions about a chart or photo, which requires understanding both the visual elements (colors, shapes, spatial relationships) and textual context to provide meaningful responses. This capability enables more intuitive data exploration and visual information retrieval. The model must recognize visual patterns, understand spatial arrangements, and interpret color encodings while simultaneously processing textual labels and contextual information provided alongside the image. This visual-textual integration requires sophisticated neural architectures that can maintain representations from both modalities and reason across them effectively.
For example, when analyzing a financial chart, the system must understand not only the visual representation of data trends but also interpret labels, legends, and axes to provide accurate insights. It needs to recognize different chart types (bar charts, line graphs, pie charts), understand what each visual element represents (rising trends, market segments, comparative data), and correctly interpret numerical scales and time periods. The system must also discern the significance of color coding (e.g., red for losses, green for gains) and pattern variations (e.g., dotted lines for projections versus solid lines for historical data), while connecting these visual cues to financial terminology and concepts in the accompanying text.
Similarly, in medical imaging, a cross-modal system can correlate visual patterns in scans with textual patient records to assist in diagnosis or treatment planning. This requires identifying subtle visual anomalies in X-rays, MRIs, or CT scans while simultaneously considering patient history, symptoms, and other clinical notes to provide contextually relevant medical analysis. The system must recognize anatomical structures, detect abnormalities like fractures, tumors, or inflammation, and understand how these visual findings relate to symptoms described in textual records. This integration enables more comprehensive clinical decision support by connecting what is seen in the image with what is known about the patient's condition.
This integration of visual and textual information also extends to other domains like geospatial analysis (interpreting maps alongside location descriptions), document understanding (processing diagrams with explanatory text), and educational content (connecting visual teaching aids with textual explanations). In geospatial applications, models must understand geographical features, topographical elements, and symbolic representations on maps while relating them to textual location descriptions, directions, or demographic data. For document understanding, the system needs to parse complex layouts with mixed text and visuals, comprehending how diagrams illustrate concepts explained in accompanying text.
The true power of multimodal systems emerges when they can seamlessly blend these different information streams into a unified understanding, allowing for more natural human-AI interaction across diverse applications. This unified comprehension enables AI systems to provide more contextually appropriate responses that consider the full range of available information, similar to how humans integrate multiple sensory inputs when understanding their environment. Through techniques like cross-attention and joint embedding spaces, these models create rich representations that capture the relationships between words and visual elements, enabling more sophisticated reasoning that mirrors human cognitive processes.
Text + Video
Explaining an event in a clip or summarizing a documentary, which demands temporal reasoning across frames while connecting visual elements to narrative structure. This integration supports content analysis at a much deeper semantic level than static image understanding, as it requires processing sequential information and understanding how scenes evolve over time. The AI must analyze multiple dimensions simultaneously - visual composition, motion patterns, temporal transitions, audio cues, and narrative progression - to construct a coherent understanding of the content.
The system must track objects and actors across time, understand causality between events, and connect visual sequences with contextual information. This tracking involves sophisticated computer vision algorithms that can maintain object identity despite changes in appearance, lighting, camera angle, or partial occlusion. For example, when analyzing a nature documentary, the model needs to recognize not just individual animals, but follow their movements across different shots, understand the narrative arc (such as a predator stalking prey), and connect these visual sequences with the documentary's educational themes. The system must interpret both explicit visual information (what is directly shown) and implicit content (what is suggested or implied through editing techniques, camera movements, or juxtaposition of scenes).
Temporal reasoning also requires understanding cinematic language - how cuts, transitions, establishing shots, close-ups, and montages contribute to storytelling. The model must recognize when a flashback occurs, when parallel storylines are being presented, or when a montage compresses time. Similarly, for news footage, the system must recognize key figures, understand the chronology of events, and place them within the broader context provided by narration or interviews. This involves correlating spoken information with visual evidence, distinguishing between primary footage and archival material, and recognizing when the same event is shown from multiple perspectives.
This multimodal reasoning enables applications like automatically generating detailed video summaries that capture both visual content and narrative structure, creating accessible descriptions for visually impaired users that convey the emotional and storytelling elements of video content, or analyzing surveillance footage with textual reports to identify specific incidents by matching visual patterns with textual descriptions of events. These applications require not just object recognition but scene understanding - comprehending the relationships between objects, their interactions, and how these elements combine to create meaning.
Advanced systems can even identify emotional arcs in film by correlating visual cinematography techniques with dialogue and music to understand how directors convey meaning through multiple channels simultaneously. This includes analyzing color grading (how warm or cool tones evoke different emotions), camera movement (steady vs. handheld to convey stability or tension), lighting techniques (high-key vs. low-key for different moods), and how these visual elements synchronize with musical cues, sound effects, and dialogue to create a unified emotional experience.
The ultimate goal is to develop AI systems that can "watch" and "understand" video content with a level of comprehension approaching that of human viewers, interpreting both denotative content (what is literally shown) and connotative meaning (what is symbolically or emotionally conveyed).
Example Use Cases:
- Accessibility: automatic captioning of lectures that combines audio transcription with slide descriptions, making educational content more accessible to people with hearing impairments or those learning in noisy environments. The system must synchronize the verbal explanation with relevant visual content.
This requires sophisticated speech recognition that can handle technical terminology and different accents, while also identifying the context of what's being discussed by analyzing visual slides. The technology must accurately timestamp speech to align with corresponding visual elements, creating a seamless experience that mimics how in-person attendees process the lecture.
- Education: tutoring systems that can explain a diagram while narrating, creating more engaging and comprehensive learning experiences by linking visual concepts with verbal explanations. These systems can adapt to different learning styles and provide multimodal reinforcement of complex concepts.
For example, when teaching molecular biology, the system could highlight specific parts of a cell diagram while verbally explaining their functions, then dynamically adjust its teaching approach based on student comprehension signals. This multimodal approach helps students form stronger mental models by connecting abstract concepts with visual representations, significantly enhancing knowledge retention compared to single-mode instruction.
- Robotics: interpreting both visual signals and verbal instructions simultaneously, enabling more natural human-robot interaction in collaborative environments. This allows robots to understand contextual commands like "pick up the red cup on the left" by combining vision processing with language understanding.
This integration is critical for assistive robots in healthcare, manufacturing, and household settings, where they must navigate complex, dynamic environments while responding to human directives that reference objects in physical space. Advanced systems can also interpret human gestures, facial expressions, and environmental cues alongside verbal commands, creating more intuitive and efficient human-robot collaboration that doesn't require humans to adapt their natural communication style.
5.3.6 Why This Matters
Video adds the dimension of time, allowing AI to model cause and effect. This temporal dimension enables AI systems to understand sequences of events, track objects through space, and recognize patterns that unfold over time. Unlike static images, video provides context about how actions lead to consequences, how objects interact, and how scenes transform. When processing video, AI can analyze motion trajectories, temporal correlations, and dynamic changes that reveal deeper insights about physical phenomena and behavioral patterns.
This capability is crucial for applications like autonomous driving (predicting pedestrian movements based on gait patterns and historical trajectory), security systems (detecting unusual behavior patterns by comparing current activities against established norms), and healthcare (analyzing patient movements in physical therapy to assess recovery progress and provide real-time feedback). The temporal reasoning enabled by video analysis allows AI to understand not just what is happening in a single moment, but how events unfold over time, creating a more complete understanding of complex scenarios.
By processing multiple frames in sequence, AI can learn to anticipate what might happen next based on what it has observed, similar to how humans develop intuitive physics. This predictive capability stems from the model's ability to extract temporal dependencies between consecutive frames, identifying cause-effect relationships and recurring patterns.
For example, in sports analysis, AI can predict player movements based on historical behavior, while in weather forecasting, it can identify evolving cloud formations that indicate changing weather conditions. This temporal understanding is fundamental to creating AI systems that can interact meaningfully with our dynamic world.
Cross-modal reasoning allows AI to integrate multiple senses, mirroring human perception. Just as humans simultaneously process what they see, hear, and read to form a complete understanding of their environment, multimodal AI systems can correlate information across different input types. This integration enables more robust understanding - when one modality provides unclear information, others can compensate. This capability represents a fundamental shift from traditional AI systems that process each sensory input independently to a more holistic approach that considers the relationships and interdependencies between different forms of information.
The power of cross-modal reasoning lies in its ability to leverage complementary information from different sources, similar to how humans instinctively combine multiple sensory inputs to navigate complex environments. By establishing correlations between visual patterns, auditory signals, and textual descriptions, AI systems can develop a more nuanced understanding of the world that transcends the limitations of any single modality. This approach allows the system to be more resilient to noise or ambiguity in individual channels by drawing on the strengths of other available inputs.
For example, in noisy environments, visual lip reading can enhance speech recognition, while in visually complex scenes, audio cues can help identify important elements. In clinical settings, AI systems can correlate medical images with written patient histories and verbal descriptions from healthcare providers to form more comprehensive diagnostic assessments. During video conference analysis, the system can integrate facial expressions, voice tone, and textual chat to better understand participant engagement and emotional states.
This cross-modal reasoning also allows AI to understand concepts more deeply by connecting abstract descriptions (text) with concrete sensory experiences (images, sounds), creating richer mental representations that more closely resemble human understanding. When an AI system can connect the textual description of "rustling leaves" with both visual imagery of moving foliage and the corresponding audio, it develops a more complete conceptual understanding than would be possible through any single modality alone. This multi-dimensional representation enables more sophisticated reasoning about real-world scenarios and more intuitive interaction with human users.
Together, these directions push LLMs closer to being general AI assistants, not just text predictors. By expanding beyond text-only processing, these systems can interact with the world more naturally and comprehensively. They can analyze and discuss visual content, process information that unfolds over time, understand speech in context, and integrate these diverse inputs into coherent responses.
This broader perception allows AI assistants to handle tasks that require understanding the physical world - from helping visually impaired users navigate environments to assisting professionals in analyzing complex multimodal data like medical scans with patient histories. The evolution toward true multimodal understanding represents a significant step toward AI systems that can perceive and reason about the world in ways that more closely align with human cognitive capabilities.
5.3.7 Looking Ahead
The frontier of multimodal AI is moving toward true integration: models that seamlessly blend text, vision, audio, and video in a single framework. This represents a significant evolution beyond current approaches where separate models handle different modalities or where models specialize in specific pairings like text-image or audio-text. True integration means developing neural architectures that process all modalities simultaneously through shared attention mechanisms and unified embedding spaces, allowing information to flow freely across different sensory channels.
These integrated models can process multiple input streams in parallel while maintaining awareness of how they relate to each other contextually. For example, understanding that a speaker's gestures on video correspond to specific concepts mentioned in their speech, or that a diagram shown in a presentation directly illustrates a verbal explanation. This cross-modal attention enables much richer understanding than processing each stream independently.
Instead of switching between specialized systems, one unified model could seamlessly process and analyze multiple forms of content simultaneously, providing a truly integrated understanding:
- Watch a video lecture, tracking visual demonstrations, facial expressions, and board work. This visual processing would include recognizing the instructor's gestures that emphasize key points, identifying when they're directing attention to specific areas, and understanding visual demonstrations that illustrate complex concepts. The model would also track changes on boards or screens, understanding how written content evolves over time.
- Listen to the narration, including tonal emphasis, pauses, and verbal cues that signal important concepts. This audio processing would detect changes in vocal pitch and volume that indicate emphasis, recognize rhetorical questions versus literal ones, understand when pauses signal transitions between topics, and identify verbal markers like "importantly" or "remember this" that highlight critical information.
- Read the slides, processing textual content, diagrams, charts, and their spatial relationships. This would involve understanding how bullet points relate hierarchically, interpreting complex visualizations like flowcharts or graphs, recognizing when text labels correspond to visual elements, and comprehending how the spatial arrangement of information conveys structural relationships between concepts.
- Summarize everything in plain English, integrating insights from all modalities into a coherent narrative. This would combine information from all sources, resolving conflicts when different modalities present contradictory information, prioritizing content based on emphasis across modalities, and presenting a unified understanding that captures the essential knowledge from all sources in a human-readable format.
These capabilities go far beyond simple feature extraction from different modalities. They represent a fundamental shift in how AI systems process and integrate information across sensory channels. While traditional multimodal systems might separately process text, images, and audio before combining their outputs, truly integrated multimodal models employ sophisticated cross-attention mechanisms that allow information to flow bidirectionally between modalities throughout the entire processing pipeline.
These cross-attention mechanisms enable several critical functions: They can dynamically align corresponding elements across modalities (matching spoken words with relevant visual objects), establish semantic connections between different representations of the same concept (connecting the word "dog" with both its visual appearance and the sound of barking), and detect discrepancies when information from different modalities appears contradictory (recognizing when spoken instructions conflict with visual demonstrations).
The resolution of these complex relationships into a unified understanding requires models to develop abstract representations that capture meaning independently of the source modality. This allows the system to identify when information in one modality complements, reinforces, or contradicts information in another, and to make reasoned judgments about how to integrate these various inputs.
For instance, when a lecturer says "as you can see in this graph" while pointing to a chart, the model must perform a complex series of operations: it must process the audio to extract the verbal reference, track the physical gesture through visual processing, identify the chart as the object being referenced, analyze the chart's content, and then integrate all this information into a coherent semantic representation that connects the verbal explanation with the visual data. This requires temporal alignment (matching when words are spoken with when gestures occur), spatial alignment (connecting the gesture to the specific area of the chart), and semantic alignment (understanding how the spoken explanation relates to the visual information).
These are the kinds of sophisticated capabilities being pioneered in research labs today through several innovative approaches:
Multiway transformers that process different modalities in parallel while allowing attention to flow between them, enabling each modality to influence how others are processed. These architectures extend the traditional transformer design by implementing specialized encoding pathways for each modality (text, image, audio, video) while maintaining cross-modal attention mechanisms. For example, when processing a video lecture, the visual pathway might attend to important visual elements while simultaneously receiving attention signals from the audio pathway that processes the speaker's voice, creating a dynamic feedback loop between modalities.
Shared embedding spaces that map inputs from different modalities into a common representational format where relationships between concepts can be directly compared regardless of their source. These unified semantic spaces enable the model to recognize that the word "apple," an image of an apple, and the sound of someone biting into an apple all refer to the same underlying concept. This approach creates a language-agnostic representation that captures meaning beyond the surface-level characteristics of any particular modality, allowing the model to transfer knowledge across modalities and reason about concepts at an abstract level.
Contrastive learning techniques that teach models to recognize when different modal representations refer to the same underlying concept by bringing their embeddings closer together in the shared space. These methods work by training the model to minimize the distance between representations of semantically related inputs (like an image of a dog and the text "a golden retriever playing") while maximizing the distance between unrelated inputs. Advanced implementations use techniques like CLIP (Contrastive Language-Image Pre-training), which learns powerful visual representations by training on millions of image-text pairs, enabling zero-shot recognition of visual concepts based on their textual descriptions.
These approaches are further enhanced by techniques like cross-modal attention masking (selectively focusing on relevant parts of each modality), modality-specific preprocessing layers (handling the unique characteristics of each input type), and sophisticated alignment strategies that synchronize temporal information across modalities with different sampling rates.
Together, these advanced architectural innovations hint at the future of cross-sensory intelligence - AI systems that can perceive and process information in ways that more closely resemble human cognition, where our understanding of the world emerges from the integration of all our senses working in concert. This holistic processing allows for more robust comprehension that leverages complementary information across modalities, enables more natural human-AI interaction that doesn't require humans to adapt their communication style to the system, and supports more sophisticated reasoning about real-world situations that inherently involve multiple sensory dimensions.
5.3 Video and Cross-Modal Research Directions
So far, we've seen how multimodal models can read text, interpret images, and even listen to audio. But the real world unfolds in time. Understanding video means not just recognizing objects in frames, but reasoning about motion, causality, and events across time. This temporal dimension adds tremendous complexity because it requires models to track entities as they move, change, appear, and disappear throughout a sequence.
For instance, understanding a cooking video requires tracking ingredients as they transform through various stages of preparation. Similarly, comprehending sports footage demands following players as they move across a field and interact with each other and the ball. These capabilities go far beyond static image recognition, requiring the model to maintain a coherent understanding of objects' identities and relationships as they evolve through time.
At the same time, many real-world tasks require more than one sense at once. A teacher may speak while showing slides, or a person may gesture while giving instructions. This is where cross-modal reasoning becomes essential — connecting multiple streams of input into a unified interpretation. Humans naturally integrate information across senses, allowing us to connect a speaker's voice to their facial movements, associate sounds with their visual sources, and understand demonstrations that combine verbal explanations with visual examples.
Creating AI systems with this capability requires sophisticated architectures that can not only process each modality effectively but also align and integrate information across them. For example, when watching an instructional video, the system must synchronize the spoken narration with the corresponding visual demonstrations, understanding which words refer to which objects or actions on screen. This form of cross-modal grounding is fundamental to creating truly helpful assistants that can understand and engage with the world as humans do.
5.3.1 Video Understanding with Transformers
Unlike images, video is a sequence of frames — and sequences are exactly what transformers excel at. The challenge is that video sequences are often huge: a 10-second clip at 30 FPS has 300 frames. Feeding all of that directly into a transformer would be computationally impossible with current hardware limitations.
For context, many image-based transformers struggle with processing even a few high-resolution images simultaneously, so handling hundreds of sequential frames would require exponentially more memory and processing power. The computational complexity grows quadratically with sequence length due to the self-attention mechanism, where each token must attend to every other token. With video, this problem is magnified dramatically.
To illustrate the scale of this challenge: if a single high-resolution frame requires processing 1,024 tokens (a modest estimate), then a 10-second video at 30 FPS would need to process 307,200 tokens simultaneously. The self-attention computation for this would involve approximately 94 billion token-to-token comparisons. Even with modern GPUs and TPUs, this is prohibitively expensive in terms of both memory requirements and computational time.
This computational burden becomes even more significant when we consider real-world applications. For instance, video analysis for security surveillance might involve processing hours of footage, potentially generating millions of frames. Similarly, content moderation systems for social media platforms need to analyze thousands of videos uploaded every minute. The sheer volume of data makes naive approaches to video processing with transformers unfeasible.
The memory requirements also present a substantial barrier. Self-attention matrices grow quadratically with input length, so a video with twice as many frames requires four times the memory. Modern GPUs typically have 16-80GB of VRAM, which would be quickly exhausted by even modest-length videos processed at full resolution. This memory constraint has forced researchers to develop specialized architectures and optimization techniques specifically for video understanding.
Additionally, video data presents unique temporal dependencies that span across frames. While a transformer could theoretically capture these relationships, the sheer volume of cross-frame connections creates a computational bottleneck that requires innovative architectural solutions beyond simply scaling up existing image transformer models.
Techniques to Handle Video
Frame sampling
Select only key frames or use a sliding window. This approach reduces computational load by choosing representative frames at regular intervals (e.g., every 5th frame) or focusing on frames with significant visual changes. While this sacrifices some temporal detail, it captures the essential content while making processing feasible.
Frame sampling is particularly effective when videos contain redundant information across consecutive frames. For example, in a surveillance video where the scene remains mostly static, processing every frame would be wasteful. By intelligently selecting frames, models can maintain high accuracy while dramatically reducing processing requirements.
The selection process can employ various strategies beyond simple fixed-interval sampling. Adaptive sampling techniques can analyze motion vectors or pixel differences between frames to determine when important changes occur. This allows more frames to be sampled during high-action sequences and fewer during static scenes, optimizing the information-to-computation ratio.
Additionally, sliding window approaches maintain temporal continuity by processing overlapping sets of frames. Rather than treating each frame in isolation, these methods analyze short sequences (e.g., 8-16 frames) at a time, sliding the window forward to progress through the video. This preserves short-term temporal relationships while keeping computation manageable.
In more detail, frame sampling works by strategically selecting a subset of frames from the complete video sequence. There are several methods for this selection, each with its own advantages for different video analysis scenarios:
- Uniform sampling: Taking frames at fixed intervals (e.g., one frame per second) to provide an even representation across the entire video. This approach is computationally efficient and works well for videos with consistent action or gradual changes. Uniform sampling reduces the computational burden by processing only a fraction of the total frames while maintaining temporal coverage across the entire video duration.When implementing uniform sampling, researchers typically define a sampling rate based on factors like video length, content type, and available computational resources.
For instance, action-heavy videos might require higher sampling rates (e.g., 2-3 frames per second) to capture quick movements, while slow-changing scenes might need only one frame every few seconds.The main advantage of uniform sampling is its simplicity and predictability. Since frames are selected at regular intervals, the model receives a consistent temporal distribution that spans the entire video without bias toward any particular segment. This helps prevent overfitting to specific temporal regions and ensures the model learns patterns that generalize across the entire timeline.
For example, in a wildlife documentary tracking animal migration, capturing one frame every few seconds can adequately represent the overall movement patterns while significantly reducing processing requirements. This approach would effectively showcase the gradual progression of herds across landscapes without needing to process every minute detail of movement between consecutive frames. The sampling rate can be adjusted based on the speed of migration – faster movements might require more frequent sampling, while slower journeys could be represented with fewer frames.
- Content-aware sampling: Using algorithms to detect significant visual changes and selecting frames only when meaningful transitions occur. This is particularly useful for videos with static scenes interrupted by important events.These methods analyze frame-to-frame differences in features like color histograms, edge patterns, or motion vectors to identify when something interesting happens. In surveillance footage, for instance, this approach might capture frames only when a person enters the frame, ignoring long periods where nothing changes.Content-aware sampling works by establishing baseline metrics for the visual content, then continuously monitoring for deviations that exceed predefined thresholds.
For example, the system might calculate the pixel-wise difference between consecutive frames, the change in distribution of colors, or the emergence of new edge patterns that could indicate new objects.More sophisticated implementations use computer vision techniques such as object detection and tracking to identify semantically meaningful changes. Rather than just measuring raw pixel differences, these systems can recognize when a new person appears, when an object moves significantly, or when the overall scene composition changes.
The computational efficiency gained through content-aware sampling can be dramatic. In a typical 24-hour surveillance video where activity occurs for only 30 minutes total, this approach might reduce the processing load by 97%, while still capturing all relevant events. This makes real-time video analysis feasible even with limited computing resources.Beyond surveillance, content-aware sampling proves valuable in domains like autonomous driving (capturing frames when traffic conditions change), medical monitoring (detecting significant patient movements), and sports analytics (identifying key plays in lengthy game footage).
- Keyframe extraction: Identifying frames that contain the most representative or information-rich content, often based on visual features or scene boundaries. These algorithms use techniques like clustering, where frames are grouped based on visual similarity, and the most central frame from each cluster is selected. This approach effectively condenses videos into their essential visual components while discarding redundant or transitional frames.
The clustering process typically involves converting each frame into feature vectors using techniques like convolutional neural networks (CNNs), then applying algorithms such as k-means or hierarchical clustering to group similar frames. Once clusters are formed, the frame closest to each cluster's centroid is selected as the keyframe, providing a diverse yet comprehensive sampling of the video's visual content.
For example, in a 30-minute documentary, keyframe extraction might identify just 20-30 frames that collectively represent all the major scenes, locations, and subjects, drastically reducing the processing requirements while preserving the core visual narrative.
Advanced methods may incorporate semantic understanding to identify frames that best capture the narrative elements of a video, such as those showing critical actions in a sports highlight or key emotional moments in a movie scene. These approaches go beyond low-level visual features to consider higher-level concepts like object interactions, facial expressions, and scene composition.
Modern keyframe extraction systems often employ deep learning models trained to recognize important visual moments based on millions of human-annotated videos. This allows them to prioritize frames with storytelling significance rather than just visual distinctiveness. For instance, in an interview video, the system might select frames showing important gestures or facial reactions rather than visually different but narratively insignificant background changes.
Some systems also incorporate additional contextual cues like audio peaks, subtitle changes, or scene transitions to better identify moments of importance. This multimodal approach ensures that keyframes align with significant developments in the video's content rather than just visual variations.
The computational benefits are substantial. For example, processing just 10% of frames can reduce memory requirements by 90% and computation time by a similar amount. This makes previously impossible tasks manageable with current hardware.
However, there are tradeoffs to consider. Fast-moving objects might appear to "teleport" between sampled frames, and subtle movements might be missed entirely. Researchers mitigate these issues by combining frame sampling with optical flow estimation or interpolation techniques that can reconstruct information about the skipped frames.
Temporal embeddings
Add position encodings for time as well as space. This technique extends the transformer's position embeddings to include temporal information, allowing the model to understand both where objects are located within frames and how they move across frames. These encodings help the model distinguish between identical frames appearing at different points in a sequence.
Temporal embeddings are crucial for video understanding because they provide essential context about when events occur in a sequence. Just as spatial position embeddings help transformers understand the arrangement of elements within an image, temporal embeddings encode the chronological order and relative timing of frames. This temporal awareness is particularly important when analyzing activities that unfold over time, like a person throwing a ball or a car turning at an intersection.
Without such temporal context, a model would struggle to differentiate between similar-looking frames that appear at different times in a video. For instance, in a cooking video where ingredients are added to a pot multiple times, the model needs to understand the sequence of additions to correctly interpret the recipe steps. Temporal embeddings provide this critical ordering information.
These embeddings can be implemented in several ways. One approach uses sinusoidal functions similar to those in the original transformer architecture, but with an additional dimension for time. Another method employs learnable embeddings specifically trained to capture temporal relationships. Some advanced systems use a combination of absolute time position (the frame's position in the entire sequence) and relative timing information (how far apart frames are from each other).
The sinusoidal approach has the advantage of being able to generalize to sequence lengths not seen during training, while learnable embeddings often capture more nuanced temporal patterns but may struggle with very long sequences. Researchers often experiment with both approaches to find the optimal solution for specific video understanding tasks.
Some advanced implementations also incorporate temporal embeddings at multiple scales. For instance, they might encode information about a frame's position within a second, a minute, and the entire video. This multi-scale approach helps models understand both fine-grained actions and longer narrative arcs within videos.
For example, in a video of a basketball game, temporal embeddings would help the model recognize that a player jumping, then releasing a ball, followed by the ball moving through a hoop represents a shooting sequence. Without temporal embeddings, these frames might be interpreted as disconnected events rather than a coherent action. The embeddings provide the critical temporal context that links these frames into a meaningful sequence.
Similarly, in a surveillance video, temporal embeddings allow the model to track individuals across frames and understand the progression of activities. This capability is essential for applications like activity recognition, where the order of actions defines the activity (e.g., entering a building versus leaving it involves the same frames in reverse order).
Hierarchical modeling
First process frames locally, then reason globally across segments. This multi-level approach initially treats smaller chunks of consecutive frames as units for local processing, extracting features about motion and changes. This hierarchical structure mirrors how humans understand videos - we first comprehend small actions and then connect them into larger narratives. The hierarchical approach is inspired by cognitive science research showing that human perception operates at multiple temporal scales simultaneously, from millisecond reactions to minute-long comprehension of complex scenes.
At the local level, models typically process 8-16 consecutive frames using lightweight attention mechanisms. This allows the model to capture short-term dynamics like object movement, facial expressions, or scene transitions without requiring extensive computational resources. These local processors extract rich representations that summarize what's happening in each small segment of video. The temporal receptive field at this level is carefully balanced - too few frames would miss important motion patterns, while too many would increase computational burden exponentially. Research has shown that 8-16 frames typically provides sufficient context to identify atomic actions while remaining computationally feasible.
These local processors employ specialized architectures like factorized attention or 3D convolutions that efficiently model spatiotemporal relationships. Some implementations use causal masking to ensure the model only attends to current and past frames, enabling real-time processing for applications like autonomous driving or security monitoring. Others process bidirectionally to maximize information extraction for offline analysis.
Then, a higher-level transformer processes these compressed representations to understand longer-term patterns and relationships across the entire video, effectively compressing the temporal dimension while preserving critical information. This global processor receives the local features as input and applies attention across them, enabling the model to recognize complex patterns like cause-effect relationships, recurring motifs, or narrative arcs that span minutes rather than seconds. The global transformer's design often includes specialized mechanisms for handling temporal distance, such as relative position encodings that help the model understand how far apart events occur in time.
This multi-resolution approach also addresses the challenge of variable information density in videos. Action-packed segments might require more detailed analysis, while static scenes need less processing. Advanced implementations dynamically allocate computational resources based on content complexity, spending more computation on informative segments.
For example, in a cooking video, local processing might identify individual actions like "chopping vegetables" or "stirring pot," while global processing would connect these into the complete recipe sequence and understand the relationship between early preparation steps and the final dish. This two-tier approach dramatically reduces computational complexity compared to processing all frames simultaneously while maintaining the ability to capture both fine-grained motions and long-range dependencies. In practical terms, this hierarchical design can reduce memory requirements by 80-90% compared to flat attention across all frames, making it possible to analyze longer videos on standard hardware.
5.3.2 VideoGPT
VideoGPT is a generative model for video built on the transformer architecture, advancing beyond static image generation by incorporating temporal aspects. This model adapts the powerful capabilities of transformer-based language models to the video domain, enabling it to understand and generate complex visual sequences over time. By extending the core principles of text generation to video, VideoGPT demonstrates how the transformer paradigm can be effectively applied across different modalities.
VideoGPT treats video as a sequence of image tokens, converting each frame into a discrete representation that can be processed sequentially. This tokenization process typically involves using a VQ-VAE (Vector Quantized Variational Autoencoder) to compress video frames into a more manageable representation. The resulting tokens form a vocabulary of visual elements that the model can manipulate, similar to how language models work with word tokens. This compression step is crucial because it reduces the dimensionality of the raw video data from millions of pixel values to thousands of discrete tokens, making the subsequent modeling task computationally feasible while preserving essential visual information.
The tokenization process works by training the VQ-VAE to encode frames into a latent space and then quantizing these continuous representations into a finite set of discrete codes from a learned codebook. This codebook effectively becomes the "visual vocabulary" of the model. The transformer component of VideoGPT then learns to predict the next token in this sequence, capturing both short-term frame-to-frame transitions and longer-term visual narratives.
The model learns both spatial and temporal dynamics, enabling frame prediction and video synthesis. This dual capability allows VideoGPT to understand not just what objects appear in a scene (spatial understanding), but how they move and interact over time (temporal understanding). The spatial component handles the composition within individual frames—elements like object appearance, lighting, and scene layout. The temporal component captures motion patterns, object persistence, and causal relationships between events across frames.
This architecture employs masked modeling techniques similar to those used in language models, where certain tokens are hidden during training and the model must predict them. In VideoGPT's case, this might involve masking future frames and training the model to predict them based on past frames, essentially learning to forecast how scenes will evolve.
For example, it can predict how a bouncing ball will continue its trajectory or how water will flow in a stream. This makes VideoGPT useful for applications like video completion, prediction of future frames, and even fully generative video synthesis from text prompts. Beyond these creative applications, the model's understanding of visual dynamics can benefit fields like robotics (predicting physical interactions), autonomous driving (anticipating traffic movements), and virtual reality (generating realistic environmental responses).
Example: Implementing VideoGPT: Video Generation with Transformers
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import numpy as np
import os
from tqdm import tqdm
# 1. VQ-VAE for Video Frame Tokenization
class VQVAE(nn.Module):
def __init__(self, input_dim=3, hidden_dim=128, num_embeddings=1024, embedding_dim=64):
super().__init__()
# Encoder: Convert raw frames to continuous latent space
self.encoder = nn.Sequential(
nn.Conv2d(input_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_dim, embedding_dim, kernel_size=3, stride=1, padding=1)
)
# Vector Quantization: Map continuous vectors to discrete codebook entries
self.codebook = nn.Embedding(num_embeddings, embedding_dim)
self.codebook.weight.data.uniform_(-1.0 / num_embeddings, 1.0 / num_embeddings)
# Decoder: Reconstruct frames from quantized tokens
self.decoder = nn.Sequential(
nn.Conv2d(embedding_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(hidden_dim, hidden_dim, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(hidden_dim, input_dim, kernel_size=4, stride=2, padding=1),
nn.Tanh()
)
def encode(self, x):
z = self.encoder(x)
return z
def quantize(self, z):
# Reshape z for quantization
z_flattened = z.permute(0, 2, 3, 1).contiguous().view(-1, z.shape[1])
# Calculate distances to codebook vectors
d = torch.sum(z_flattened**2, dim=1, keepdim=True) + \
torch.sum(self.codebook.weight**2, dim=1) - \
2 * torch.matmul(z_flattened, self.codebook.weight.t())
# Find nearest codebook vector
min_encoding_indices = torch.argmin(d, dim=1)
z_q = self.codebook(min_encoding_indices).view(z.shape[0], z.shape[2], z.shape[3], z.shape[1])
z_q = z_q.permute(0, 3, 1, 2).contiguous()
# Straight-through estimator for gradients
z_q_sg = z + (z_q - z).detach()
return z_q_sg, min_encoding_indices.view(z.shape[0], z.shape[2], z.shape[3])
def decode(self, z_q):
return self.decoder(z_q)
def forward(self, x):
z = self.encode(x)
z_q_sg, indices = self.quantize(z)
x_recon = self.decode(z_q_sg)
return x_recon, z, z_q_sg, indices
# 2. Transformer for Video Prediction
class VideoGPTTransformer(nn.Module):
def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6,
dim_feedforward=2048, max_seq_length=256):
super().__init__()
self.d_model = d_model
# Token embedding: Convert discrete tokens to continuous vectors
self.token_embedding = nn.Embedding(vocab_size, d_model)
# Position encoding: Add information about token position in sequence
self.pos_encoder = nn.Parameter(torch.zeros(1, max_seq_length, d_model))
# Transformer encoder layers
encoder_layers = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True
)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
# Output head: Project to token probabilities
self.output_head = nn.Linear(d_model, vocab_size)
def forward(self, src, src_mask=None):
# src shape: [batch_size, seq_len]
batch_size, seq_len = src.shape
# Embed tokens and add positional encoding
src = self.token_embedding(src) * np.sqrt(self.d_model)
src = src + self.pos_encoder[:, :seq_len, :]
# Pass through transformer
output = self.transformer_encoder(src, src_mask)
# Project to vocabulary space
output = self.output_head(output)
return output
# 3. Dataset for processing video frames
class VideoDataset(Dataset):
def __init__(self, video_dir, frame_size=(64, 64), frames_per_clip=16, transform=None):
self.video_paths = [os.path.join(video_dir, f) for f in os.listdir(video_dir)
if f.endswith(('.mp4', '.avi'))]
self.frame_size = frame_size
self.frames_per_clip = frames_per_clip
self.transform = transform or transforms.Compose([
transforms.Resize(frame_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
def __len__(self):
return len(self.video_paths)
def __getitem__(self, idx):
import cv2
video_path = self.video_paths[idx]
cap = cv2.VideoCapture(video_path)
# Calculate frame sampling
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_indices = np.linspace(0, total_frames-1, self.frames_per_clip, dtype=int)
# Extract frames
frames = []
for frame_idx in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
ret, frame = cap.read()
if ret:
# Convert BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Apply transforms
if self.transform:
frame = self.transform(frame)
frames.append(frame)
cap.release()
# Stack frames along a new dimension
return torch.stack(frames) # Shape: [frames_per_clip, channels, height, width]
# 4. Training Functions
def train_vqvae(vqvae, dataloader, optimizer, epochs=10, device='cuda'):
vqvae.to(device)
for epoch in range(epochs):
total_loss = 0
for batch_idx, frames in enumerate(tqdm(dataloader)):
frames = frames.to(device) # [B, T, C, H, W]
batch_size, time_steps = frames.shape[:2]
# Reshape to process all frames at once
frames_flat = frames.view(-1, *frames.shape[2:]) # [B*T, C, H, W]
optimizer.zero_grad()
# Forward pass through VQ-VAE
x_recon, z, z_q, indices = vqvae(frames_flat)
# Calculate losses
recon_loss = F.mse_loss(x_recon, frames_flat)
# VQ-VAE commitment loss
vq_loss = F.mse_loss(z_q, z.detach())
commitment_loss = F.mse_loss(z, z_q.detach())
# Combined loss
loss = recon_loss + vq_loss + 0.25 * commitment_loss
# Backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
return vqvae
def train_transformer(transformer, vqvae, dataloader, optimizer, epochs=10, device='cuda'):
transformer.to(device)
vqvae.to(device).eval()
for epoch in range(epochs):
total_loss = 0
for batch_idx, frames in enumerate(tqdm(dataloader)):
frames = frames.to(device) # [B, T, C, H, W]
batch_size, time_steps = frames.shape[:2]
# Reshape to process all frames at once
frames_flat = frames.view(-1, *frames.shape[2:]) # [B*T, C, H, W]
# Get token indices from VQ-VAE
with torch.no_grad():
z = vqvae.encode(frames_flat)
_, indices = vqvae.quantize(z)
# Reshape indices back to [batch_size, time_steps, height, width]
indices = indices.view(batch_size, time_steps, *indices.shape[1:])
# Flatten spatial dimensions to get sequence of tokens per frame
# [batch_size, time_steps, height*width]
token_sequences = indices.reshape(batch_size, time_steps, -1)
# For transformer training, we predict next tokens
src = token_sequences[:, :-1].reshape(batch_size, -1) # Input sequence
tgt = token_sequences[:, 1:].reshape(batch_size, -1) # Target sequence
optimizer.zero_grad()
# Create attention mask (optional for training efficiency)
seq_len = src.shape[1]
attn_mask = torch.triu(torch.ones(seq_len, seq_len) * float('-inf'), diagonal=1).to(device)
# Forward pass
output = transformer(src, attn_mask)
# Calculate loss
loss = F.cross_entropy(output.reshape(-1, output.size(-1)), tgt.reshape(-1))
# Backward pass
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
return transformer
# 5. Main: Putting it all together
def main():
# Hyperparameters
batch_size = 8
frames_per_clip = 16
frame_size = (64, 64)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Create dataset and dataloader
dataset = VideoDataset(
video_dir="path/to/videos",
frame_size=frame_size,
frames_per_clip=frames_per_clip
)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)
# Step 1: Train VQ-VAE
vqvae = VQVAE(input_dim=3, hidden_dim=128, num_embeddings=1024, embedding_dim=64)
vqvae_optimizer = torch.optim.Adam(vqvae.parameters(), lr=3e-4)
vqvae = train_vqvae(vqvae, dataloader, vqvae_optimizer, epochs=10, device=device)
# Save VQ-VAE model
torch.save(vqvae.state_dict(), "vqvae_model.pth")
# Step 2: Train Transformer
# Number of tokens = codebook size (from VQ-VAE)
vocab_size = 1024 + 1 # +1 for padding token
transformer = VideoGPTTransformer(vocab_size=vocab_size, d_model=512, nhead=8, num_layers=6)
transformer_optimizer = torch.optim.Adam(transformer.parameters(), lr=1e-4)
transformer = train_transformer(transformer, vqvae, dataloader, transformer_optimizer, epochs=20, device=device)
# Save Transformer model
torch.save(transformer.state_dict(), "transformer_model.pth")
# 6. Video Generation Function
def generate_video(vqvae, transformer, seed_frames, num_frames_to_generate=16, device='cuda'):
vqvae.to(device).eval()
transformer.to(device).eval()
# Process seed frames through VQ-VAE to get tokens
with torch.no_grad():
seed_frames = seed_frames.to(device)
z = vqvae.encode(seed_frames)
_, indices = vqvae.quantize(z)
# Flatten spatial dimensions to get sequence of tokens
token_sequence = indices.reshape(1, -1) # [1, time*height*width]
# Generate new frames one by one
generated_tokens = token_sequence.clone()
for _ in range(num_frames_to_generate):
# Predict next tokens
with torch.no_grad():
output = transformer(generated_tokens)
next_token_logits = output[:, -1, :]
next_tokens = torch.argmax(next_token_logits, dim=-1, keepdim=True)
generated_tokens = torch.cat([generated_tokens, next_tokens], dim=1)
# Extract only the newly generated tokens
new_tokens = generated_tokens[:, token_sequence.shape[1]:]
# Reshape tokens to match expected input for VQ-VAE decoder
h, w = indices.shape[1], indices.shape[2]
new_tokens = new_tokens.reshape(-1, h, w) # [num_frames_to_generate, height, width]
# Decode tokens to frames
generated_frames = []
with torch.no_grad():
for tokens in new_tokens:
tokens = tokens.unsqueeze(0) # Add batch dimension
z_q = vqvae.codebook(tokens.view(-1)).view(1, -1, vqvae.codebook.embedding_dim)
z_q = z_q.permute(0, 2, 1).view(1, vqvae.codebook.embedding_dim, h, w)
frame = vqvae.decode(z_q)
generated_frames.append(frame)
# Stack frames along time dimension
return torch.cat(generated_frames, dim=0) # [num_frames_to_generate, C, H, W]
if __name__ == "__main__":
main()
Detailed Explanation of VideoGPT Implementation
This example demonstrates a comprehensive approach to video generation using the VideoGPT architecture. Let's break down the key components:
1. Vector Quantized Variational Autoencoder (VQ-VAE)
The VQ-VAE forms the foundation of VideoGPT by converting raw video frames into discrete tokens:
- Encoder: Compresses video frames into a lower-dimensional continuous latent space using convolutional layers.
- Vector Quantization: Maps these continuous vectors to the nearest vectors in a learned "codebook," effectively discretizing the representation.
- Decoder: Reconstructs the original frames from the quantized representations.
- Straight-through estimator: A technique used during training to allow gradients to flow through the non-differentiable quantization step.
This tokenization process is crucial because it reduces the dimensionality of video data from millions of pixel values to a more manageable set of discrete tokens, making the subsequent modeling task computationally feasible.
2. Transformer Architecture
Once the video frames are tokenized, a transformer model predicts the next tokens in sequence:
- Token Embedding: Converts discrete tokens into continuous vector representations.
- Positional Encoding: Adds information about each token's position in the sequence.
- Transformer Encoder: Processes the token embeddings using self-attention mechanisms to capture dependencies between tokens.
- Output Head: Projects the transformer's output back to token probabilities for prediction.
The transformer architecture allows the model to understand complex spatial-temporal patterns within videos, capturing both short-term frame-to-frame transitions and longer-term visual narratives.
3. Dataset Handling
The custom VideoDataset class handles video processing:
- Extracts frames from video files at regular intervals.
- Applies transformations (resize, normalize) to prepare frames for the model.
- Packages frames into clips of a specified length.
4. Training Process
The training happens in two distinct phases:
- VQ-VAE Training: Optimizes the encoder, codebook, and decoder to effectively compress and reconstruct video frames while building a meaningful discrete representation.
- Transformer Training: After the VQ-VAE is trained, video frames are tokenized and fed to the transformer, which learns to predict future tokens based on past ones.
5. Video Generation
The generation process reverses the training pipeline:
- Seed frames are tokenized through the VQ-VAE encoder and quantizer.
- The transformer autoregressively generates new tokens one by one.
- These tokens are then decoded back into video frames using the VQ-VAE decoder.
Key Technical Insights
- Two-stage Architecture: Separating representation learning (VQ-VAE) from sequence modeling (transformer) makes training more stable and efficient.
- Spatial-Temporal Modeling: The model must capture both spatial relationships within frames and temporal dependencies across frames.
- Autoregressive Generation: Videos are generated one token at a time, with each new token conditioned on all previous tokens.
- Computational Efficiency: Working with discrete tokens rather than raw pixels drastically reduces the computational requirements.
This implementation demonstrates how transformer architectures, originally designed for language modeling, can be effectively adapted to video generation by incorporating appropriate tokenization strategies and handling the additional complexity of temporal data.
5.3.3 Gemini (DeepMind)
Gemini (DeepMind) is a sophisticated multimodal model that seamlessly integrates text, vision, and in some cases video within a unified architecture. Unlike earlier models that treated different data types in isolation, Gemini processes and reasons across multiple input formats simultaneously. This represents a significant advancement over previous approaches where text, images, and video were often processed by separate specialized models and then combined afterward. This unified approach allows Gemini to understand the contextual relationships between different modalities from the ground up rather than trying to merge separately processed information.
The model employs advanced cross-attention mechanisms that enable it to scale effectively across modalities. These attention mechanisms allow the model to identify relationships between elements in different formats—for example, connecting a textual description to relevant parts of an image or linking dialogue to visual events in a video sequence. This architecture enables information to flow bidirectionally between modalities, creating a more holistic understanding. Unlike simple concatenation of different input embeddings, Gemini's cross-attention system allows for dynamic weighting of information across modalities based on context and relevance, similar to how humans naturally shift focus between what they see and hear. This dynamic attention system helps the model determine which aspects of an image might be most relevant to a textual query, or conversely, which parts of a text prompt should inform the understanding of visual content.
Gemini demonstrates impressive reasoning capabilities across a wide range of multimodal inputs, including complex diagrams (such as scientific illustrations or technical schematics), video content (with temporal understanding), and multifaceted prompts that combine several input types. The model can process images at high resolution, enabling it to recognize fine details in photographs, charts, and documents.
For video analysis, Gemini can track objects over time, understand narrative progression, and even anticipate likely future developments based on visual dynamics. This capability is particularly valuable in scenarios requiring detailed visual analysis, such as interpreting medical imagery, understanding engineering diagrams, or analyzing sports footage to extract tactical insights.
This reasoning extends beyond simple recognition to include causal understanding, spatial relationships, and temporal sequences—allowing the model to answer questions like "What will happen next in this physical system?" or "How does this mechanism work?" while referencing visual material. The model's temporal understanding is crucial for tasks that involve processes unfolding over time, such as explaining chemical reactions, analyzing mechanical systems, or tracking changes in biological specimens. This capability resembles human experts' ability to "read" dynamic systems from static diagrams or limited video inputs.
Gemini's multimodal capabilities enable it to solve complex tasks requiring synthesis across modalities, such as interpreting a graph while considering textual context, explaining the steps of a visual process, or identifying inconsistencies between spoken narration and visual content. This integrated approach mirrors human cognition more closely than previous AI systems, as it can form connections between concepts across different representational formats.
This integration facilitates more natural human-AI interaction, allowing users to communicate with the system using whatever combination of text, images, or video best suits their needs, rather than being constrained to a single modality. For example, a user could ask Gemini to analyze a chart, compare it with historical data mentioned in an accompanying text, and explain apparent discrepancies—a task that requires seamless integration of visual and textual information.
Example: Gemini Implementation
import google.generativeai as genai
import PIL.Image
import os
from IPython.display import display, HTML
# Configure API key
genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
# List available models
for m in genai.list_models():
if 'generateContent' in m.supported_generation_methods:
print(m.name)
# Select Gemini Pro Vision model
model = genai.GenerativeModel('gemini-pro-vision')
# Function to analyze an image with text prompt
def analyze_image(image_path, prompt):
img = PIL.Image.open(image_path)
response = model.generate_content([prompt, img])
return response.text
# Function for multimodal reasoning with multiple images
def compare_images(image_path1, image_path2, prompt):
img1 = PIL.Image.open(image_path1)
img2 = PIL.Image.open(image_path2)
response = model.generate_content([prompt, img1, img2])
return response.text
# Example usage: Image analysis
image_analysis = analyze_image("chart.jpg",
"Analyze this chart in detail. What trends do you observe?")
print(image_analysis)
# Example usage: Image comparison
comparison = compare_images("design_v1.jpg", "design_v2.jpg",
"Compare these two design versions and explain the key differences.")
print(comparison)
# Example: Complex reasoning with image and specific instructions
reasoning = analyze_image("scientific_diagram.jpg",
"Explain how this biological process works. Focus on:
1. The starting materials
2. The transformation steps
3. The end products
4. The energy changes involved")
print(reasoning)
# Example: Video frame analysis
def analyze_video_frames(frame_paths, prompt):
frames = [PIL.Image.open(path) for path in frame_paths]
response = model.generate_content([prompt] + frames)
return response.text
frame_paths = ["video_frame1.jpg", "video_frame2.jpg", "video_frame3.jpg"]
video_analysis = analyze_video_frames(frame_paths,
"Analyze the motion sequence shown in these frames. What's happening?")
print(video_analysis)
# Safety settings example (optional)
safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_ONLY_HIGH"
}
]
model_with_safety = genai.GenerativeModel(
model_name='gemini-pro-vision',
safety_settings=safety_settings
)
Understanding the Gemini Implementation
The code example above demonstrates how to work with Google's Gemini multimodal model, providing a practical framework for integrating vision and language understanding. Let's explore the key components and capabilities:
API Configuration and Model Selection
The implementation begins by importing the necessary libraries and configuring the API with an authentication key. The code then lists available models with content generation capabilities before selecting the Gemini Pro Vision model, which is specifically designed for multimodal tasks combining text and images.
Core Functionality
The implementation provides several functions that showcase Gemini's multimodal capabilities:
- Single Image Analysis: The
analyze_image()function accepts an image path and a text prompt, then returns Gemini's interpretation of the image in the context of the prompt. This enables tasks like chart analysis, object identification, or scene description. - Comparative Image Analysis: With
compare_images(), the model can reason about relationships between multiple images, identifying similarities, differences, and patterns across visual content. This is useful for before/after comparisons, design iterations, or tracking changes. - Video Frame Analysis: Though Gemini doesn't process video directly in this implementation, the
analyze_video_frames()function demonstrates how to analyze temporal sequences by feeding multiple frames with a contextual prompt. This allows for basic motion analysis and event understanding across time.
Prompt Engineering for Multimodal Tasks
The example showcases several prompt structures that enable different types of visual reasoning:
- Open-ended analysis: "Analyze this chart in detail. What trends do you observe?" allows the model to identify and describe patterns with minimal constraints.
- Comparative analysis: "Compare these two design versions and explain the key differences" directs the model to focus specifically on contrasting visual elements.
- Structured reasoning: The scientific diagram prompt uses a numbered list to guide the model through a systematic analysis process, ensuring comprehensive coverage of specific aspects.
- Temporal understanding: "Analyze the motion sequence shown in these frames" encourages the model to consider relationships between images as representing a continuous process rather than isolated visuals.
Safety Considerations
The implementation includes optional safety settings that can be configured to control the model's outputs according to different harm categories and thresholds. This demonstrates how to implement responsible AI practices when deploying multimodal systems that might encounter or generate sensitive content.
Technical Significance
What makes this implementation particularly powerful is its simplicity relative to the complexity of the underlying model. The Gemini architecture internally handles the complex cross-attention mechanisms that align visual and textual information, allowing developers to interact with it through a straightforward API.
Unlike previous approaches that required separate models for vision and language tasks, Gemini's unified architecture enables it to process both modalities jointly, capturing the interactions between them. This is evident in how a single function call can pass both text and images to the model and receive coherent, contextually relevant responses.
Practical Applications
This implementation enables numerous real-world applications:
- Data visualization interpretation: Automatically generating insights from charts, graphs, and other visual data representations.
- Document understanding: Analyzing documents that combine text and images, such as technical manuals, academic papers, or illustrated guides.
- Educational content analysis: Processing instructional materials that use diagrams and text explanations to convey complex concepts.
- Design feedback: Providing structured analysis of visual designs, identifying issues and suggesting improvements.
- Medical image preliminary assessment: Assisting healthcare professionals by providing initial observations on medical imagery alongside clinical notes.
The example demonstrates how Gemini bridges the gap between computer vision and natural language processing, offering an integrated approach to understanding the visual world through the lens of language and vice versa.
5.3.4 Kosmos-2 (Microsoft)
Focuses on grounding language in vision, which means creating explicit connections between language descriptions and specific visual elements. This technique enables the model to understand not just what objects are in an image, but precisely where they are located and how they relate to linguistic references. The model essentially creates a detailed spatial map of the image, connecting language tokens directly to pixel regions. This grounding capability represents a fundamental shift in how AI processes visual information, moving from general scene understanding to precise object localization and reference resolution—similar to how humans point at objects while describing them. Just as a person might say "look at that red bird on the branch" while pointing, Kosmos-2 can conceptually "point" to objects it describes.
Can link words to objects in an image or video frame, enabling tasks like "point to the cat in the video." This capability represents a significant advancement over earlier models that could only describe images generally but couldn't identify specific regions or elements when prompted. For example, when asked "What is the person on the left wearing?", Kosmos-2 can both understand the spatial reference ("on the left") and ground its response to the specific person being referenced. It can generate bounding boxes or segmentation masks that highlight exactly which pixels in the image correspond to "the person on the left" before answering about their clothing. This requires sophisticated visual reasoning that combines object detection, spatial awareness, and natural language understanding in a unified framework—a computational challenge that previous generations of models struggled to address. The model must simultaneously parse language, recognize objects, understand spatial relationships, and maintain the connections between them all.
A step toward cross-modal grounding, where models tie abstract descriptions to concrete visual elements. This connection between language and vision mimics how humans naturally communicate about visual information, allowing for more precise visual reasoning, improved human-AI interaction, and the foundation for embodied AI systems that need to understand references to objects in their environment. Rather than treating language and vision as separate domains that occasionally interact, Kosmos-2 builds a shared representational space where concepts from either modality can be mapped directly to each other. The grounding capability is especially valuable for applications like visual question answering, image editing based on natural language instructions, and assistive technologies for the visually impaired. For instance, a visually impaired user could ask "Is there a cup on the table?" and receive not just a yes/no answer, but information about where exactly the cup is located relative to other objects.
By establishing direct links between words and visual regions, Kosmos-2 creates a foundation for more sophisticated reasoning tasks that require understanding both the semantics of language and the spatial configuration of visual scenes—capabilities that are essential for robots navigating physical environments, AR/VR systems responding to natural language commands about visible objects, or accessibility tools that help visually impaired users understand their surroundings through verbal descriptions. This grounding mechanism also enables multi-turn interactions about specific parts of an image, where a user might ask "What's in the corner?" followed by "What color is it?" and the model correctly maintains context about which object is being discussed. The alignment between language and vision provides a crucial building block for AI systems that must operate in the physical world, where understanding references to objects and their relationships is fundamental to meaningful interaction.
Example: Kosmos-2 Implementation
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import requests
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# Load Kosmos-2 model and processor
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
# Function to get image from URL
def get_image(url):
image = Image.open(requests.get(url, stream=True).raw)
return image
# Function to process image and generate caption with bounding boxes
def analyze_with_grounding(image, prompt="<grounding>Describe this image in detail:"):
# Process the image and text
inputs = processor(text=prompt, images=image, return_tensors="pt")
# Generate output from model
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
num_beams=5,
early_stopping=True
)
# Decode the generated text with grounding tokens
generated_text = processor.batch_decode(outputs, skip_special_tokens=False)[0]
# Extract bounding boxes from the generated output
phrase_bboxes = []
for phrase, bbox in processor.parse_bbox(generated_text):
if bbox is not None:
phrase_bboxes.append((phrase, bbox))
return generated_text, phrase_bboxes
# Function to visualize the image with bounding boxes
def visualize_with_bboxes(image, phrase_bboxes):
plt.figure(figsize=(16, 10))
plt.imshow(image)
ax = plt.gca()
# Add bounding boxes with labels
for phrase, bbox in phrase_bboxes:
x, y, width, height = bbox
rect = patches.Rectangle(
(x * image.width, y * image.height),
width * image.width,
height * image.height,
linewidth=2,
edgecolor='r',
facecolor='none'
)
ax.add_patch(rect)
plt.text(
x * image.width,
y * image.height - 5,
phrase,
color='white',
backgroundcolor='red',
fontsize=10
)
plt.axis('off')
plt.tight_layout()
plt.show()
# Function for comparing objects in an image
def compare_objects(image, prompt="<grounding>Compare the objects in this image:"):
generated_text, phrase_bboxes = analyze_with_grounding(image, prompt)
print("Generated Text:", generated_text)
visualize_with_bboxes(image, phrase_bboxes)
return generated_text, phrase_bboxes
# Function for referring expression comprehension
def find_specific_object(image, object_description):
prompt = f"<grounding>Point to the {object_description} in this image."
generated_text, phrase_bboxes = analyze_with_grounding(image, prompt)
print(f"Looking for: {object_description}")
print("Generated Text:", generated_text)
visualize_with_bboxes(image, phrase_bboxes)
return generated_text, phrase_bboxes
# Example usage
image_url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/living_room.jpg"
image = get_image(image_url)
# Basic image description with grounding
description, bboxes = analyze_with_grounding(image)
print("Description with grounding:")
print(description)
visualize_with_bboxes(image, bboxes)
# Find a specific object
find_specific_object(image, "red couch")
# Compare objects
compare_objects(image, "<grounding>Compare the furniture items in this image.")
# Spatial reasoning example
spatial_reasoning = find_specific_object(image, "lamp next to the couch")
Understanding the Kosmos-2 Implementation
The code example above demonstrates how to work with Microsoft's Kosmos-2 multimodal model, showcasing its unique capability for visual grounding. Let's break down the key components and capabilities:
Setup and Initialization
The implementation begins by importing the necessary libraries and initializing the Kosmos-2 model and processor from the Hugging Face Transformers library. Kosmos-2 is accessed through the AutoModelForVision2Seq class, which handles models that can process both vision and language.
Core Grounding Functionality
The central function analyze_with_grounding() demonstrates Kosmos-2's key innovation: the ability to connect language descriptions with specific visual elements through grounding. The function:
Processes an image along with a prompt that includes the special `` token to activate the model's grounding capabilities
- Generates a descriptive response about the image
- Extracts bounding box coordinates for objects that the model has identified and mentioned
- Returns both the generated text and a list of phrase-bounding box pairs
Visual Grounding in Action
The visualize_with_bboxes() function provides a visualization capability that overlays the model's detected objects on the original image. This visual representation shows how Kosmos-2 connects its language understanding with precise spatial locations in the image, effectively demonstrating the model's ability to "point" at objects it's describing.
Advanced Visual Reasoning Capabilities
The implementation includes specialized functions that showcase different aspects of Kosmos-2's visual reasoning abilities:
- Object Comparison: The
compare_objects()function prompts the model to identify and compare multiple objects in an image, highlighting each with bounding boxes. This demonstrates the model's ability to reason about relationships between different visual elements. - Referring Expression Comprehension: With
find_specific_object(), the model locates specific objects based on natural language descriptions. This capability is essential for tasks requiring precise object localization based on verbal instructions. - Spatial Reasoning: The example shows how Kosmos-2 can understand spatial relationships between objects (e.g., "lamp next to the couch"), combining object recognition with positional awareness.
Prompt Engineering for Grounding
The example highlights the importance of the `` token in prompts, which serves as a special instruction to the model to activate its visual grounding capabilities. Different prompting strategies demonstrate various aspects of visual reasoning:
- "Describe this image in detail" triggers comprehensive scene understanding with object localization
- "Point to the [object]" focuses the model on locating a specific item
- "Compare the objects" encourages the model to identify multiple entities and reason about their similarities and differences
Technical Significance
What makes Kosmos-2 particularly innovative is its ability to create explicit connections between natural language descriptions and specific regions in an image. Unlike earlier multimodal models that could generally describe an image but couldn't pinpoint specific objects, Kosmos-2's grounding mechanism enables:
- Precise object localization in response to natural language queries
- Fine-grained understanding of spatial relationships between objects
- The ability to answer questions about specific parts of an image
- More natural human-AI interaction by mimicking how humans point while describing
Practical Applications
This implementation of Kosmos-2 enables numerous real-world applications:
- Assistive technology: Helping visually impaired users understand their surroundings by describing specific objects and their locations
- Visual search: Finding objects in images based on natural language descriptions
- Human-robot interaction: Enabling robots to understand references to objects in their environment
- Visual question answering: Providing detailed answers about specific elements in an image
- Educational tools: Creating interactive learning experiences that connect visual concepts with language
Kosmos-2 represents an important step toward AI systems that can perceive and reason about the visual world in ways that more closely resemble human understanding, bridging the gap between seeing and communicating about what is seen.
Example: Extracting Features from Video with Hugging Face
We can’t yet run full Gemini or Kosmos in open-source, but we can use pretrained models like VideoMAE for video embeddings.
from transformers import VideoMAEFeatureExtractor, VideoMAEModel
import torch
import av # pip install av
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os
import time
def load_video_mae_model():
"""Load the pretrained VideoMAE model and feature extractor"""
print("Loading VideoMAE model...")
feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
return feature_extractor, model
def extract_frames(video_path, num_frames=8, sample_rate=30):
"""Extract frames from a video file at a specific sample rate
Args:
video_path: Path to the video file
num_frames: Maximum number of frames to extract
sample_rate: Extract every nth frame
Returns:
List of frames as numpy arrays in RGB format
"""
print(f"Extracting frames from {video_path}...")
if not os.path.exists(video_path):
raise FileNotFoundError(f"Video file not found: {video_path}")
container = av.open(video_path)
frames = []
# Get video info
video_stream = container.streams.video[0]
fps = video_stream.average_rate
duration = container.duration / 1000000 # in seconds
total_frames = video_stream.frames
print(f"Video info: {fps} fps, {duration:.2f}s duration, {total_frames} total frames")
start_time = time.time()
for i, frame in enumerate(container.decode(video=0)):
if i % sample_rate == 0:
frames.append(frame.to_ndarray(format="rgb24"))
print(f"Extracted frame {len(frames)}/{num_frames} (video position: {i})")
if len(frames) == num_frames:
break
process_time = time.time() - start_time
print(f"Frame extraction complete. Extracted {len(frames)} frames in {process_time:.2f}s")
return frames
def get_video_embeddings(feature_extractor, model, frames):
"""Process frames and extract embeddings using VideoMAE
Args:
feature_extractor: VideoMAE feature extractor
model: VideoMAE model
frames: List of video frames as numpy arrays
Returns:
Video embeddings tensor and raw model outputs
"""
if len(frames) == 0:
raise ValueError("No frames were extracted from the video")
print(f"Processing {len(frames)} frames with VideoMAE...")
# Preprocess frames
inputs = feature_extractor(frames, return_tensors="pt")
# Extract embeddings
with torch.no_grad():
outputs = model(**inputs)
video_embeddings = outputs.last_hidden_state
return video_embeddings, outputs
def visualize_frames_and_embeddings(frames, embeddings):
"""Visualize extracted frames and a 2D PCA projection of their embeddings"""
# Visualize frames
num_frames = len(frames)
fig, axes = plt.subplots(1, num_frames, figsize=(16, 4))
for i, (frame, ax) in enumerate(zip(frames, axes)):
ax.imshow(frame)
ax.set_title(f"Frame {i}")
ax.axis('off')
plt.tight_layout()
plt.savefig("video_frames.png")
plt.show()
# Visualize embedding patterns (simple 2D visualization)
# Take mean over tokens dimension to get per-frame representations
frame_embeddings = embeddings.mean(dim=1).squeeze(0)
# PCA-like dimensionality reduction (simplified)
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
reduced_embeddings = pca.fit_transform(frame_embeddings.numpy())
plt.figure(figsize=(8, 6))
plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1])
# Add frame numbers
for i, (x, y) in enumerate(reduced_embeddings):
plt.annotate(str(i), (x, y), fontsize=12)
plt.title("2D projection of frame embeddings")
plt.xlabel("Component 1")
plt.ylabel("Component 2")
plt.grid(True, linestyle='--', alpha=0.7)
plt.savefig("embedding_visualization.png")
plt.show()
def compute_frame_similarity(embeddings):
"""Compute cosine similarity between frame embeddings"""
# Mean over token dimension to get per-frame embeddings
frame_embeddings = embeddings.mean(dim=1).squeeze(0)
# Normalize embeddings
norm = frame_embeddings.norm(dim=1, keepdim=True)
normalized_embeddings = frame_embeddings / norm
# Compute similarity matrix
similarity = torch.mm(normalized_embeddings, normalized_embeddings.t())
# Visualize similarity matrix
plt.figure(figsize=(8, 6))
plt.imshow(similarity.numpy(), cmap='viridis')
plt.colorbar(label='Cosine Similarity')
plt.title("Frame-to-Frame Similarity")
plt.xlabel("Frame Index")
plt.ylabel("Frame Index")
plt.savefig("frame_similarity.png")
plt.show()
return similarity
def detect_scene_changes(similarity_matrix, threshold=0.8):
"""Simple scene change detection based on frame similarity"""
# Check if adjacent frames are below similarity threshold
scene_changes = []
sim_np = similarity_matrix.numpy()
for i in range(len(sim_np) - 1):
if sim_np[i, i+1] < threshold:
scene_changes.append(i+1)
print(f"Detected {len(scene_changes)} potential scene changes at frames: {scene_changes}")
return scene_changes
def main():
# Load model
feature_extractor, model = load_video_mae_model()
# Process video
video_path = "sample_video.mp4"
frames = extract_frames(video_path, num_frames=8, sample_rate=30)
# Get embeddings
video_embeddings, outputs = get_video_embeddings(feature_extractor, model, frames)
print("Video embeddings shape:", video_embeddings.shape) # [batch, frames, hidden_dim]
# Visualize frames and embeddings
visualize_frames_and_embeddings(frames, video_embeddings)
# Compute and visualize frame similarity
similarity = compute_frame_similarity(video_embeddings)
# Detect scene changes
scene_changes = detect_scene_changes(similarity, threshold=0.8)
print("Processing complete!")
if __name__ == "__main__":
main()
The example above demonstrates a comprehensive approach to working with videos in machine learning contexts using the VideoMAE (Video Masked Autoencoder) model. VideoMAE is a self-supervised learning framework for video understanding that works by reconstructing masked portions of video frames. Let's break down the key components:
Video Frame Extraction: The code uses the PyAV library to efficiently decode and extract frames from video files at specified intervals. This is crucial for video processing since working with every frame would be computationally expensive and often redundant, as adjacent frames typically contain similar information.
Feature Extraction with VideoMAE: The extracted frames are processed through VideoMAE, which transforms the raw pixel data into high-dimensional feature vectors (embeddings). These embeddings capture semantic information about objects, actions, and scenes present in the video.
Visualization Components: The code includes several visualization functions that help understand both the raw video content (displaying extracted frames) and the encoded representations (embedding visualizations). This is valuable for debugging and gaining insights into how the model "sees" the video.
Frame Similarity Analysis: By computing cosine similarity between frame embeddings, the code can identify how similar or different consecutive frames are. This has practical applications in scene boundary detection, content summarization, and keyframe extraction.
Scene Change Detection: A simple threshold-based approach is implemented to detect potential scene changes, which could be useful for video indexing, summarization, or creating chapter markers.
The code represents a foundation for more complex video understanding tasks like action recognition, video captioning, or video question answering. These capabilities are essential for applications ranging from content moderation and video search to assistive technologies for the visually impaired.
When working with the VideoMAE model, it's important to understand that:
- The model's input preprocessing is specific and requires frames to be in a particular format and dimension.
- The output embeddings are hierarchical and capture different levels of temporal and spatial information.
- The token dimension in the output shape represents the spatial tokens that the image is divided into.
- For downstream tasks, you would typically need to apply additional processing or fine-tuning to adapt these generic embeddings for specific purposes.
This code example provides a solid starting point for exploring multimodal capabilities that bridge the gap between computer vision and natural language processing, which is increasingly important as AI systems need to understand the world in ways that more closely resemble human perception.
5.3.5 Cross-Modal Reasoning
Cross-modal reasoning goes beyond processing modalities in isolation. It's about integration - the ability to synthesize and analyze information across different perceptual channels simultaneously. This represents a significant advancement over systems that can only process one type of input at a time, as it mirrors how humans naturally perceive and understand the world around them.
Unlike traditional AI systems that handle each input type separately, cross-modal models create a unified understanding by establishing connections between different types of information, enabling more comprehensive and contextual analysis. This integration happens at a deep representational level, where the model learns to map concepts across modalities into a shared semantic space.
For example, when a cross-modal system processes both an image of a dog and the word "dog," it doesn't treat these as separate, unrelated inputs. Instead, it recognizes they refer to the same concept despite coming through different perceptual channels. This ability to form these cross-modal associations is fundamental to human cognition and represents a crucial step toward more human-like AI understanding.
The technical implementation of cross-modal reasoning often involves complex neural architectures with shared embedding spaces, cross-attention mechanisms, and fusion techniques that preserve the unique characteristics of each modality while enabling information to flow between them. These systems must learn not just to process each modality effectively but to identify meaningful correlations between them, distinguishing relevant connections from coincidental ones.
Audio + Video:
Lip-reading and voice alignment, where models can match spoken words with mouth movements to improve speech recognition in noisy environments or for hearing-impaired users. This integration allows for more robust communication understanding. The system analyzes both the visual cues of lip movements and the acoustic properties of speech, compensating for deficiencies in either modality.
When processing lip movements, these systems track facial landmarks and mouth shapes that correspond to specific phonemes (speech sounds). Meanwhile, the audio component analyzes spectral and temporal features of the speech signal. By combining these streams of information, the system can disambiguate similar-sounding phonemes that have distinct visual representations (like "ba" vs "fa") or clarify unclear audio by leveraging the visual channel.
Advanced models employ attention mechanisms that dynamically weight the importance of visual versus audio inputs depending on their reliability. For instance, when ambient noise increases, the system automatically places greater emphasis on visual information. Conversely, in low-light conditions where visual data is less reliable, the audio channel receives higher priority.
This is particularly valuable in crowded settings where background noise might otherwise make speech recognition impossible, or in assistive technologies for people with hearing impairments who rely partly on visual cues for communication. In teleconferencing applications, this technology helps maintain clear communication even with unstable internet connections by reconstructing parts of the message from the available modality.
Example: Cross-Modal Reasoning: Audio + Video Integration
import torch
import torchaudio
import torch.nn as nn
import torch.nn.functional as F
import av
import numpy as np
import matplotlib.pyplot as plt
from transformers import Wav2Vec2Processor, Wav2Vec2Model
from transformers import VideoMAEFeatureExtractor, VideoMAEModel
from sklearn.metrics.pairwise import cosine_similarity
from PIL import Image
import librosa
import librosa.display
class AudioVideoSyncModel(nn.Module):
"""
A model for audio-video synchronization and cross-modal reasoning
"""
def __init__(self, audio_dim=768, video_dim=768, joint_dim=512):
super().__init__()
self.audio_projection = nn.Linear(audio_dim, joint_dim)
self.video_projection = nn.Linear(video_dim, joint_dim)
self.cross_attention = nn.MultiheadAttention(
embed_dim=joint_dim,
num_heads=8,
batch_first=True
)
self.classifier = nn.Sequential(
nn.Linear(joint_dim, 256),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(256, 1),
nn.Sigmoid()
)
def forward(self, audio_features, video_features):
"""
Process audio and video features and compute synchronization score
Args:
audio_features: Tensor of shape [batch_size, seq_len_audio, audio_dim]
video_features: Tensor of shape [batch_size, seq_len_video, video_dim]
Returns:
sync_score: Synchronization probability between 0-1
joint_features: Cross-modal features after attention
"""
# Project to common space
audio_proj = self.audio_projection(audio_features)
video_proj = self.video_projection(video_features)
# Apply cross-attention from video to audio
joint_features, _ = self.cross_attention(
query=video_proj,
key=audio_proj,
value=audio_proj
)
# Get global representation by mean pooling
global_joint = torch.mean(joint_features, dim=1)
# Predict synchronization score
sync_score = self.classifier(global_joint)
return sync_score, joint_features
def extract_video_frames(video_path, sample_rate=5):
"""
Extract frames from a video at regular intervals
Args:
video_path: Path to video file
sample_rate: Sample every nth frame
Returns:
List of frames as numpy arrays in RGB format
"""
frames = []
try:
container = av.open(video_path)
stream = container.streams.video[0]
total_frames = stream.frames
fps = float(stream.average_rate)
print(f"Video: {total_frames} frames, {fps} fps")
for i, frame in enumerate(container.decode(video=0)):
if i % sample_rate == 0:
# Convert to RGB numpy array
img = frame.to_ndarray(format='rgb24')
frames.append(img)
print(f"Extracted {len(frames)} frames")
container.close()
except Exception as e:
print(f"Error extracting video frames: {e}")
return frames
def extract_audio_from_video(video_path, target_sr=16000):
"""
Extract audio from a video file
Args:
video_path: Path to video file
target_sr: Target sampling rate
Returns:
Audio waveform and sample rate
"""
try:
container = av.open(video_path)
audio_stream = container.streams.audio[0]
# Initialize an empty numpy array to store audio samples
audio_data = []
# Decode audio
for frame in container.decode(audio=0):
# Convert PyAV AudioFrame to numpy array
frame_data = frame.to_ndarray()
audio_data.append(frame_data)
# Concatenate audio frames
if audio_data:
audio_array = np.concatenate(audio_data)
# Convert to mono if stereo
if len(audio_array.shape) > 1 and audio_array.shape[1] > 1:
audio_array = np.mean(audio_array, axis=1)
# Resample if needed
original_sr = audio_stream.rate
if original_sr != target_sr:
audio_resampled = librosa.resample(
audio_array,
orig_sr=original_sr,
target_sr=target_sr
)
return audio_resampled, target_sr
return audio_array, original_sr
else:
raise ValueError("No audio frames found")
except Exception as e:
print(f"Error extracting audio: {e}")
return None, None
def process_video(video_path, video_model, video_processor, sample_rate=5):
"""
Extract and process video frames
Args:
video_path: Path to video file
video_model: VideoMAE model
video_processor: VideoMAE feature extractor
sample_rate: Sample every nth frame
Returns:
Video features tensor
"""
# Extract frames
frames = extract_video_frames(video_path, sample_rate)
if not frames:
raise ValueError("No frames were extracted")
# Process frames with VideoMAE
inputs = video_processor(frames, return_tensors="pt")
with torch.no_grad():
outputs = video_model(**inputs)
video_features = outputs.last_hidden_state
return video_features, frames
def process_audio(audio_array, sr, audio_model, audio_processor):
"""
Process audio with Wav2Vec2
Args:
audio_array: Audio samples as numpy array
sr: Sample rate
audio_model: Wav2Vec2 model
audio_processor: Wav2Vec2 processor
Returns:
Audio features tensor
"""
# Prepare audio for Wav2Vec2
inputs = audio_processor(
audio_array,
sampling_rate=sr,
return_tensors="pt"
)
with torch.no_grad():
outputs = audio_model(**inputs)
audio_features = outputs.last_hidden_state
return audio_features
def detect_audiovisual_sync(sync_scores, threshold=0.5):
"""
Analyze synchronization scores to detect in-sync vs out-of-sync segments
Args:
sync_scores: List of synchronization scores
threshold: Threshold for considering audio-video in sync
Returns:
List of in-sync and out-of-sync segments
"""
segments = []
current_segment = {"start": 0, "status": "sync" if sync_scores[0] >= threshold else "out-of-sync"}
for i in range(1, len(sync_scores)):
current_status = "sync" if sync_scores[i] >= threshold else "out-of-sync"
previous_status = "sync" if sync_scores[i-1] >= threshold else "out-of-sync"
if current_status != previous_status:
# End the previous segment
current_segment["end"] = i - 1
segments.append(current_segment)
# Start a new segment
current_segment = {"start": i, "status": current_status}
# Add the final segment
current_segment["end"] = len(sync_scores) - 1
segments.append(current_segment)
return segments
def visualize_sync_analysis(frames, audio_waveform, sr, sync_scores, segments):
"""
Visualize audio, video frames, and synchronization analysis
Args:
frames: List of video frames
audio_waveform: Audio samples
sr: Audio sample rate
sync_scores: Synchronization scores
segments: Detected sync/out-of-sync segments
"""
fig, axes = plt.subplots(3, 1, figsize=(15, 10), gridspec_kw={'height_ratios': [1, 1, 2]})
# Plot synchronization scores
axes[0].plot(sync_scores)
axes[0].set_ylim(0, 1)
axes[0].set_ylabel('Sync Score')
axes[0].set_xlabel('Frame')
axes[0].axhline(y=0.5, color='r', linestyle='--')
# Highlight sync/out-of-sync segments
for segment in segments:
color = 'green' if segment['status'] == 'sync' else 'red'
axes[0].axvspan(segment['start'], segment['end'], alpha=0.2, color=color)
# Plot audio waveform
librosa.display.waveshow(audio_waveform, sr=sr, ax=axes[1])
axes[1].set_ylabel('Amplitude')
# Display frames at key points
n_frames = min(8, len(frames))
indices = np.linspace(0, len(frames)-1, n_frames, dtype=int)
for i, idx in enumerate(indices):
ax = plt.subplot(3, n_frames, i + 2*n_frames + 1)
ax.imshow(frames[idx])
ax.set_title(f"Frame {idx}")
ax.axis('off')
plt.tight_layout()
plt.savefig('av_sync_analysis.png')
plt.show()
def demonstrate_lip_reading(sync_model, audio_features, video_features, frames):
"""
Demonstrate lip-reading by finding the most relevant audio for each video frame
using the cross-attention mechanism
Args:
sync_model: Trained AudioVideoSyncModel
audio_features: Audio features tensor
video_features: Video features tensor
frames: List of video frames
Returns:
Attention weights showing audio-visual connections
"""
# Project features to common space
audio_proj = sync_model.audio_projection(audio_features)
video_proj = sync_model.video_projection(video_features)
# Compute raw attention scores
attn_weights = torch.matmul(video_proj, audio_proj.transpose(-2, -1)) / np.sqrt(audio_proj.size(-1))
# Convert to probabilities
attn_probs = F.softmax(attn_weights, dim=-1)
# Visualize attention for selected frames
n_frames = min(4, len(frames))
indices = np.linspace(0, len(frames)-1, n_frames, dtype=int)
fig, axes = plt.subplots(2, n_frames, figsize=(15, 6))
for i, idx in enumerate(indices):
# Show the frame
axes[0, i].imshow(frames[idx])
axes[0, i].set_title(f"Frame {idx}")
axes[0, i].axis('off')
# Show attention weights (which audio segments this frame attends to)
if idx < attn_probs.shape[1]: # Ensure index is valid
axes[1, i].plot(attn_probs[0, idx].detach().numpy())
axes[1, i].set_title("Audio Attention")
axes[1, i].set_xlabel("Audio Frames")
axes[1, i].set_ylabel("Attention Weight")
plt.tight_layout()
plt.savefig('lip_reading_attention.png')
plt.show()
return attn_probs
def main():
# Initialize models
print("Loading models...")
# Audio model (Wav2Vec2)
audio_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
audio_model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
# Video model (VideoMAE)
video_processor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
video_model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
# Cross-modal synchronization model
sync_model = AudioVideoSyncModel(
audio_dim=768, # Wav2Vec2 feature dimension
video_dim=768, # VideoMAE feature dimension
joint_dim=512 # Joint embedding dimension
)
# Process video with speaking person
video_path = "speaking_person.mp4"
print(f"Processing video: {video_path}")
# Extract audio from video
audio_array, sr = extract_audio_from_video(video_path)
if audio_array is None:
print("Failed to extract audio")
return
print(f"Audio: {len(audio_array)} samples, {sr} Hz")
# Process video frames
video_features, frames = process_video(
video_path,
video_model,
video_processor,
sample_rate=5
)
# Process audio
audio_features = process_audio(
audio_array,
sr,
audio_model,
audio_processor
)
print("Audio features shape:", audio_features.shape)
print("Video features shape:", video_features.shape)
# Simulate training the sync model (in practice, this requires proper training)
# Here we're just demonstrating the forward pass
sync_scores = []
step_size = max(1, audio_features.shape[1] // video_features.shape[1])
for i in range(video_features.shape[1]):
# Get corresponding audio chunk
start_idx = i * step_size
end_idx = min((i + 1) * step_size, audio_features.shape[1])
audio_chunk = audio_features[:, start_idx:end_idx, :]
video_frame_feat = video_features[:, i:i+1, :]
# Mean pool audio chunk
audio_chunk_pooled = torch.mean(audio_chunk, dim=1, keepdim=True)
# Get sync score
score, _ = sync_model(audio_chunk_pooled, video_frame_feat)
sync_scores.append(score.item())
# Analyze synchronization
segments = detect_audiovisual_sync(sync_scores)
print("Detected segments:")
for segment in segments:
print(f"Frames {segment['start']}-{segment['end']}: {segment['status']}")
# Visualize results
visualize_sync_analysis(frames, audio_array, sr, sync_scores, segments)
# Demonstrate lip reading capabilities
print("Generating lip reading visualization...")
attn_weights = demonstrate_lip_reading(sync_model, audio_features, video_features, frames)
print("Analysis complete!")
if __name__ == "__main__":
main()
This code example demonstrates a comprehensive approach to cross-modal reasoning with audio and video, focusing on lip-reading and speech-video synchronization analysis. Let's break down the key components:
The AudioVideoSyncModel class implements a neural network architecture that processes both audio and video features and learns to align them in a shared representation space. It uses several important mechanisms:
- Modal-specific projection layers that map audio and video features to a common semantic space
- Cross-attention mechanisms that allow the model to determine which parts of the audio correspond to which visual frames
- A classification head that predicts whether audio and video are synchronized
The extract_video_frames function extracts frames from a video at regular intervals using PyAV, which provides a Pythonic binding to the FFmpeg libraries. This sampling approach is essential for efficiency since processing every frame would be computationally expensive and often redundant for semantic understanding.
Similarly, extract_audio_from_video extracts the audio track from a video file and processes it into a format suitable for deep learning models, including converting to a consistent sampling rate and handling multi-channel audio.
The process_video and process_audio functions use pretrained models from the Transformers library to convert raw video frames and audio signals into high-dimensional feature representations:
- VideoMAE (Video Masked Autoencoder) processes video frames, extracting features that capture objects, actions, and visual context
- Wav2Vec2 processes audio, capturing phonetic and linguistic information
The detect_audiovisual_sync function analyzes the synchronization scores to identify segments where audio and video are well-aligned versus segments where they might be out of sync. This is valuable for applications like automatic correction of audio-video synchronization issues in recorded content.
The visualize_sync_analysis function creates a comprehensive visualization showing:
- The synchronization scores over time
- Color-coded segments indicating in-sync and out-of-sync portions
- The audio waveform
- Key video frames from throughout the sequence
The demonstrate_lip_reading function shows how the cross-attention mechanism effectively implements a form of lip reading by connecting mouth movements in video frames with corresponding audio segments. It visualizes the attention weights, showing which parts of the audio each video frame is most strongly associated with.
In the main function, we see the entire pipeline in action:
- Models are loaded and initialized
- Video and audio are extracted and processed
- The synchronization model is applied to analyze the alignment between modalities
- Results are visualized for interpretation
This implementation has numerous practical applications:
- Assistive technology for hearing-impaired users to enhance speech understanding with visual cues
- Video production tools that automatically detect and correct audio-video synchronization issues
- Enhanced speech recognition in noisy environments by leveraging visual information
- Security applications for detecting manipulated content where audio and video don't naturally align
- Educational tools that ensure properly synchronized content for optimal learning experiences
The example represents a foundation that could be extended with more sophisticated training procedures and architectural improvements. In a production environment, this system would require proper training data consisting of paired audio-video examples with both synchronized and deliberately misaligned samples.
Text + Image
Answering questions about a chart or photo, which requires understanding both the visual elements (colors, shapes, spatial relationships) and textual context to provide meaningful responses. This capability enables more intuitive data exploration and visual information retrieval. The model must recognize visual patterns, understand spatial arrangements, and interpret color encodings while simultaneously processing textual labels and contextual information provided alongside the image. This visual-textual integration requires sophisticated neural architectures that can maintain representations from both modalities and reason across them effectively.
For example, when analyzing a financial chart, the system must understand not only the visual representation of data trends but also interpret labels, legends, and axes to provide accurate insights. It needs to recognize different chart types (bar charts, line graphs, pie charts), understand what each visual element represents (rising trends, market segments, comparative data), and correctly interpret numerical scales and time periods. The system must also discern the significance of color coding (e.g., red for losses, green for gains) and pattern variations (e.g., dotted lines for projections versus solid lines for historical data), while connecting these visual cues to financial terminology and concepts in the accompanying text.
Similarly, in medical imaging, a cross-modal system can correlate visual patterns in scans with textual patient records to assist in diagnosis or treatment planning. This requires identifying subtle visual anomalies in X-rays, MRIs, or CT scans while simultaneously considering patient history, symptoms, and other clinical notes to provide contextually relevant medical analysis. The system must recognize anatomical structures, detect abnormalities like fractures, tumors, or inflammation, and understand how these visual findings relate to symptoms described in textual records. This integration enables more comprehensive clinical decision support by connecting what is seen in the image with what is known about the patient's condition.
This integration of visual and textual information also extends to other domains like geospatial analysis (interpreting maps alongside location descriptions), document understanding (processing diagrams with explanatory text), and educational content (connecting visual teaching aids with textual explanations). In geospatial applications, models must understand geographical features, topographical elements, and symbolic representations on maps while relating them to textual location descriptions, directions, or demographic data. For document understanding, the system needs to parse complex layouts with mixed text and visuals, comprehending how diagrams illustrate concepts explained in accompanying text.
The true power of multimodal systems emerges when they can seamlessly blend these different information streams into a unified understanding, allowing for more natural human-AI interaction across diverse applications. This unified comprehension enables AI systems to provide more contextually appropriate responses that consider the full range of available information, similar to how humans integrate multiple sensory inputs when understanding their environment. Through techniques like cross-attention and joint embedding spaces, these models create rich representations that capture the relationships between words and visual elements, enabling more sophisticated reasoning that mirrors human cognitive processes.
Text + Video
Explaining an event in a clip or summarizing a documentary, which demands temporal reasoning across frames while connecting visual elements to narrative structure. This integration supports content analysis at a much deeper semantic level than static image understanding, as it requires processing sequential information and understanding how scenes evolve over time. The AI must analyze multiple dimensions simultaneously - visual composition, motion patterns, temporal transitions, audio cues, and narrative progression - to construct a coherent understanding of the content.
The system must track objects and actors across time, understand causality between events, and connect visual sequences with contextual information. This tracking involves sophisticated computer vision algorithms that can maintain object identity despite changes in appearance, lighting, camera angle, or partial occlusion. For example, when analyzing a nature documentary, the model needs to recognize not just individual animals, but follow their movements across different shots, understand the narrative arc (such as a predator stalking prey), and connect these visual sequences with the documentary's educational themes. The system must interpret both explicit visual information (what is directly shown) and implicit content (what is suggested or implied through editing techniques, camera movements, or juxtaposition of scenes).
Temporal reasoning also requires understanding cinematic language - how cuts, transitions, establishing shots, close-ups, and montages contribute to storytelling. The model must recognize when a flashback occurs, when parallel storylines are being presented, or when a montage compresses time. Similarly, for news footage, the system must recognize key figures, understand the chronology of events, and place them within the broader context provided by narration or interviews. This involves correlating spoken information with visual evidence, distinguishing between primary footage and archival material, and recognizing when the same event is shown from multiple perspectives.
This multimodal reasoning enables applications like automatically generating detailed video summaries that capture both visual content and narrative structure, creating accessible descriptions for visually impaired users that convey the emotional and storytelling elements of video content, or analyzing surveillance footage with textual reports to identify specific incidents by matching visual patterns with textual descriptions of events. These applications require not just object recognition but scene understanding - comprehending the relationships between objects, their interactions, and how these elements combine to create meaning.
Advanced systems can even identify emotional arcs in film by correlating visual cinematography techniques with dialogue and music to understand how directors convey meaning through multiple channels simultaneously. This includes analyzing color grading (how warm or cool tones evoke different emotions), camera movement (steady vs. handheld to convey stability or tension), lighting techniques (high-key vs. low-key for different moods), and how these visual elements synchronize with musical cues, sound effects, and dialogue to create a unified emotional experience.
The ultimate goal is to develop AI systems that can "watch" and "understand" video content with a level of comprehension approaching that of human viewers, interpreting both denotative content (what is literally shown) and connotative meaning (what is symbolically or emotionally conveyed).
Example Use Cases:
- Accessibility: automatic captioning of lectures that combines audio transcription with slide descriptions, making educational content more accessible to people with hearing impairments or those learning in noisy environments. The system must synchronize the verbal explanation with relevant visual content.
This requires sophisticated speech recognition that can handle technical terminology and different accents, while also identifying the context of what's being discussed by analyzing visual slides. The technology must accurately timestamp speech to align with corresponding visual elements, creating a seamless experience that mimics how in-person attendees process the lecture.
- Education: tutoring systems that can explain a diagram while narrating, creating more engaging and comprehensive learning experiences by linking visual concepts with verbal explanations. These systems can adapt to different learning styles and provide multimodal reinforcement of complex concepts.
For example, when teaching molecular biology, the system could highlight specific parts of a cell diagram while verbally explaining their functions, then dynamically adjust its teaching approach based on student comprehension signals. This multimodal approach helps students form stronger mental models by connecting abstract concepts with visual representations, significantly enhancing knowledge retention compared to single-mode instruction.
- Robotics: interpreting both visual signals and verbal instructions simultaneously, enabling more natural human-robot interaction in collaborative environments. This allows robots to understand contextual commands like "pick up the red cup on the left" by combining vision processing with language understanding.
This integration is critical for assistive robots in healthcare, manufacturing, and household settings, where they must navigate complex, dynamic environments while responding to human directives that reference objects in physical space. Advanced systems can also interpret human gestures, facial expressions, and environmental cues alongside verbal commands, creating more intuitive and efficient human-robot collaboration that doesn't require humans to adapt their natural communication style.
5.3.6 Why This Matters
Video adds the dimension of time, allowing AI to model cause and effect. This temporal dimension enables AI systems to understand sequences of events, track objects through space, and recognize patterns that unfold over time. Unlike static images, video provides context about how actions lead to consequences, how objects interact, and how scenes transform. When processing video, AI can analyze motion trajectories, temporal correlations, and dynamic changes that reveal deeper insights about physical phenomena and behavioral patterns.
This capability is crucial for applications like autonomous driving (predicting pedestrian movements based on gait patterns and historical trajectory), security systems (detecting unusual behavior patterns by comparing current activities against established norms), and healthcare (analyzing patient movements in physical therapy to assess recovery progress and provide real-time feedback). The temporal reasoning enabled by video analysis allows AI to understand not just what is happening in a single moment, but how events unfold over time, creating a more complete understanding of complex scenarios.
By processing multiple frames in sequence, AI can learn to anticipate what might happen next based on what it has observed, similar to how humans develop intuitive physics. This predictive capability stems from the model's ability to extract temporal dependencies between consecutive frames, identifying cause-effect relationships and recurring patterns.
For example, in sports analysis, AI can predict player movements based on historical behavior, while in weather forecasting, it can identify evolving cloud formations that indicate changing weather conditions. This temporal understanding is fundamental to creating AI systems that can interact meaningfully with our dynamic world.
Cross-modal reasoning allows AI to integrate multiple senses, mirroring human perception. Just as humans simultaneously process what they see, hear, and read to form a complete understanding of their environment, multimodal AI systems can correlate information across different input types. This integration enables more robust understanding - when one modality provides unclear information, others can compensate. This capability represents a fundamental shift from traditional AI systems that process each sensory input independently to a more holistic approach that considers the relationships and interdependencies between different forms of information.
The power of cross-modal reasoning lies in its ability to leverage complementary information from different sources, similar to how humans instinctively combine multiple sensory inputs to navigate complex environments. By establishing correlations between visual patterns, auditory signals, and textual descriptions, AI systems can develop a more nuanced understanding of the world that transcends the limitations of any single modality. This approach allows the system to be more resilient to noise or ambiguity in individual channels by drawing on the strengths of other available inputs.
For example, in noisy environments, visual lip reading can enhance speech recognition, while in visually complex scenes, audio cues can help identify important elements. In clinical settings, AI systems can correlate medical images with written patient histories and verbal descriptions from healthcare providers to form more comprehensive diagnostic assessments. During video conference analysis, the system can integrate facial expressions, voice tone, and textual chat to better understand participant engagement and emotional states.
This cross-modal reasoning also allows AI to understand concepts more deeply by connecting abstract descriptions (text) with concrete sensory experiences (images, sounds), creating richer mental representations that more closely resemble human understanding. When an AI system can connect the textual description of "rustling leaves" with both visual imagery of moving foliage and the corresponding audio, it develops a more complete conceptual understanding than would be possible through any single modality alone. This multi-dimensional representation enables more sophisticated reasoning about real-world scenarios and more intuitive interaction with human users.
Together, these directions push LLMs closer to being general AI assistants, not just text predictors. By expanding beyond text-only processing, these systems can interact with the world more naturally and comprehensively. They can analyze and discuss visual content, process information that unfolds over time, understand speech in context, and integrate these diverse inputs into coherent responses.
This broader perception allows AI assistants to handle tasks that require understanding the physical world - from helping visually impaired users navigate environments to assisting professionals in analyzing complex multimodal data like medical scans with patient histories. The evolution toward true multimodal understanding represents a significant step toward AI systems that can perceive and reason about the world in ways that more closely align with human cognitive capabilities.
5.3.7 Looking Ahead
The frontier of multimodal AI is moving toward true integration: models that seamlessly blend text, vision, audio, and video in a single framework. This represents a significant evolution beyond current approaches where separate models handle different modalities or where models specialize in specific pairings like text-image or audio-text. True integration means developing neural architectures that process all modalities simultaneously through shared attention mechanisms and unified embedding spaces, allowing information to flow freely across different sensory channels.
These integrated models can process multiple input streams in parallel while maintaining awareness of how they relate to each other contextually. For example, understanding that a speaker's gestures on video correspond to specific concepts mentioned in their speech, or that a diagram shown in a presentation directly illustrates a verbal explanation. This cross-modal attention enables much richer understanding than processing each stream independently.
Instead of switching between specialized systems, one unified model could seamlessly process and analyze multiple forms of content simultaneously, providing a truly integrated understanding:
- Watch a video lecture, tracking visual demonstrations, facial expressions, and board work. This visual processing would include recognizing the instructor's gestures that emphasize key points, identifying when they're directing attention to specific areas, and understanding visual demonstrations that illustrate complex concepts. The model would also track changes on boards or screens, understanding how written content evolves over time.
- Listen to the narration, including tonal emphasis, pauses, and verbal cues that signal important concepts. This audio processing would detect changes in vocal pitch and volume that indicate emphasis, recognize rhetorical questions versus literal ones, understand when pauses signal transitions between topics, and identify verbal markers like "importantly" or "remember this" that highlight critical information.
- Read the slides, processing textual content, diagrams, charts, and their spatial relationships. This would involve understanding how bullet points relate hierarchically, interpreting complex visualizations like flowcharts or graphs, recognizing when text labels correspond to visual elements, and comprehending how the spatial arrangement of information conveys structural relationships between concepts.
- Summarize everything in plain English, integrating insights from all modalities into a coherent narrative. This would combine information from all sources, resolving conflicts when different modalities present contradictory information, prioritizing content based on emphasis across modalities, and presenting a unified understanding that captures the essential knowledge from all sources in a human-readable format.
These capabilities go far beyond simple feature extraction from different modalities. They represent a fundamental shift in how AI systems process and integrate information across sensory channels. While traditional multimodal systems might separately process text, images, and audio before combining their outputs, truly integrated multimodal models employ sophisticated cross-attention mechanisms that allow information to flow bidirectionally between modalities throughout the entire processing pipeline.
These cross-attention mechanisms enable several critical functions: They can dynamically align corresponding elements across modalities (matching spoken words with relevant visual objects), establish semantic connections between different representations of the same concept (connecting the word "dog" with both its visual appearance and the sound of barking), and detect discrepancies when information from different modalities appears contradictory (recognizing when spoken instructions conflict with visual demonstrations).
The resolution of these complex relationships into a unified understanding requires models to develop abstract representations that capture meaning independently of the source modality. This allows the system to identify when information in one modality complements, reinforces, or contradicts information in another, and to make reasoned judgments about how to integrate these various inputs.
For instance, when a lecturer says "as you can see in this graph" while pointing to a chart, the model must perform a complex series of operations: it must process the audio to extract the verbal reference, track the physical gesture through visual processing, identify the chart as the object being referenced, analyze the chart's content, and then integrate all this information into a coherent semantic representation that connects the verbal explanation with the visual data. This requires temporal alignment (matching when words are spoken with when gestures occur), spatial alignment (connecting the gesture to the specific area of the chart), and semantic alignment (understanding how the spoken explanation relates to the visual information).
These are the kinds of sophisticated capabilities being pioneered in research labs today through several innovative approaches:
Multiway transformers that process different modalities in parallel while allowing attention to flow between them, enabling each modality to influence how others are processed. These architectures extend the traditional transformer design by implementing specialized encoding pathways for each modality (text, image, audio, video) while maintaining cross-modal attention mechanisms. For example, when processing a video lecture, the visual pathway might attend to important visual elements while simultaneously receiving attention signals from the audio pathway that processes the speaker's voice, creating a dynamic feedback loop between modalities.
Shared embedding spaces that map inputs from different modalities into a common representational format where relationships between concepts can be directly compared regardless of their source. These unified semantic spaces enable the model to recognize that the word "apple," an image of an apple, and the sound of someone biting into an apple all refer to the same underlying concept. This approach creates a language-agnostic representation that captures meaning beyond the surface-level characteristics of any particular modality, allowing the model to transfer knowledge across modalities and reason about concepts at an abstract level.
Contrastive learning techniques that teach models to recognize when different modal representations refer to the same underlying concept by bringing their embeddings closer together in the shared space. These methods work by training the model to minimize the distance between representations of semantically related inputs (like an image of a dog and the text "a golden retriever playing") while maximizing the distance between unrelated inputs. Advanced implementations use techniques like CLIP (Contrastive Language-Image Pre-training), which learns powerful visual representations by training on millions of image-text pairs, enabling zero-shot recognition of visual concepts based on their textual descriptions.
These approaches are further enhanced by techniques like cross-modal attention masking (selectively focusing on relevant parts of each modality), modality-specific preprocessing layers (handling the unique characteristics of each input type), and sophisticated alignment strategies that synchronize temporal information across modalities with different sampling rates.
Together, these advanced architectural innovations hint at the future of cross-sensory intelligence - AI systems that can perceive and process information in ways that more closely resemble human cognition, where our understanding of the world emerges from the integration of all our senses working in concert. This holistic processing allows for more robust comprehension that leverages complementary information across modalities, enables more natural human-AI interaction that doesn't require humans to adapt their communication style to the system, and supports more sophisticated reasoning about real-world situations that inherently involve multiple sensory dimensions.