Quiz Part III
True or False
6. Cross-modal attention aligns embeddings from different modalities such as text and images.
True / False
7. Video summarization combines insights from audio, video frames, and text.
True / False
8. Vision-language models like CLIP are unsuitable for tasks requiring zero-shot classification.
True / False
9. Whisper is designed to handle noisy audio environments effectively.
True / False
10. Multimodal transformers rely solely on text data for training.
True / False
True or False
6. Cross-modal attention aligns embeddings from different modalities such as text and images.
True / False
7. Video summarization combines insights from audio, video frames, and text.
True / False
8. Vision-language models like CLIP are unsuitable for tasks requiring zero-shot classification.
True / False
9. Whisper is designed to handle noisy audio environments effectively.
True / False
10. Multimodal transformers rely solely on text data for training.
True / False
True or False
6. Cross-modal attention aligns embeddings from different modalities such as text and images.
True / False
7. Video summarization combines insights from audio, video frames, and text.
True / False
8. Vision-language models like CLIP are unsuitable for tasks requiring zero-shot classification.
True / False
9. Whisper is designed to handle noisy audio environments effectively.
True / False
10. Multimodal transformers rely solely on text data for training.
True / False
True or False
6. Cross-modal attention aligns embeddings from different modalities such as text and images.
True / False
7. Video summarization combines insights from audio, video frames, and text.
True / False
8. Vision-language models like CLIP are unsuitable for tasks requiring zero-shot classification.
True / False
9. Whisper is designed to handle noisy audio environments effectively.
True / False
10. Multimodal transformers rely solely on text data for training.
True / False