Summary of Ego-vpa: Egocentric Video Understanding with Parameter-efficient Adaptation, by Tz-ying Wu et al.
Ego-VPA: Egocentric Video Understanding with Parameter-efficient Adaptation
by Tz-Ying Wu, Kyle Min, Subarna Tripathi, Nuno Vasconcelos
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel approach to adapting large-scale video foundation models for egocentric video tasks, dubbed Ego-VPA. By leveraging video-language pre-training, Ego-VPA employs local sparse approximation to model context fusion and cross-modal transfer in an efficient manner. The method excels in lightweight adaptation, requiring only 0.84% learnable parameters and achieving performance comparable to full fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand videos better by making it easier for them to adapt to new situations. It uses a special kind of AI model called Ego-VPA, which is trained on both video and text data. This allows the computer to understand how different parts of the video relate to each other, and even transfer this understanding between different types of media (like images or text). The result is that computers can learn to do tasks with videos more quickly and accurately. |
Keywords
» Artificial intelligence » Fine tuning
