Summary of Motion Meets Attention: Video Motion Prompts, by Qixiang Chen et al.
Motion meets Attention: Video Motion Prompts
by Qixiang Chen, Lei Wang, Piotr Koniusz, Tom Gedeon
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed method enhances action recognition in videos by developing a modified Sigmoid function-based attention mechanism to extract precise motion features. The approach generates a sequence of attention maps that modulate motion signals from frame differencing maps, promoting the processing of motion-related content. To ensure temporal continuity and smoothness, pair-wise temporal attention variation regularization is applied to remove noise while preserving important motions. The highlighted motions, called video motion prompts, are used as inputs to the model instead of original frames, effectively bridging the gap between traditional ‘blind motion extraction’ and relevant motion extraction. This lightweight, plug-and-play motion prompt layer integrates seamlessly into models like SlowFast, X3D, and TimeSformer, enhancing performance on benchmarks such as FineGym and MPII Cooking 2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to analyze videos. It helps machines understand the movements and actions in videos better. The method uses attention mechanisms to highlight important parts of the video that show motion. This makes it easier for machines to recognize actions like people playing sports or cooking meals. The approach also removes unwanted noise from the video, making it more accurate. |
Keywords
» Artificial intelligence » Attention » Prompt » Regularization » Sigmoid
