Summary of Topa: Extending Large Language Models For Video Understanding Via Text-only Pre-alignment, by Wei Li et al.
TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-Alignment
by Wei Li, Hehe Fan, Yongkang Wong, Mohan Kankanhalli, Yi Yang
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Text-Only Pre-Alignment (TOPA) approach extends large language models (LLMs) for video understanding without requiring real video data. First, an advanced LLM generates textual videos with annotations to simulate real video-text data. Then, these annotated textual videos are used to pre-align a language-only LLM with the video modality. The CLIP model serves as the feature extractor to align image and text modalities during text-only pre-alignment. This approach achieves competitive performance on various video understanding tasks, including zero-shot evaluation and fine-tuning, without training on any video data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers understand videos is presented in this paper. Currently, it’s hard for computers to understand videos because they’re very complex and there isn’t enough good data for them to learn from. To solve this problem, the researchers created a system that generates fake videos made of text instead of images. These fake videos are then used to train a language model so it can understand real video content. The results show that this approach works well and is even better than some other methods that use more data. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Language model » Zero shot
