Summary of On the Utility Of 3d Hand Poses For Action Recognition, by Md Salman Shamil et al.
On the Utility of 3D Hand Poses for Action Recognition
by Md Salman Shamil, Dibyadip Chatterjee, Fadime Sener, Shugao Ma, Angela Yao
First submitted to arxiv on: 14 Mar 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 The paper proposes HandFormer, a novel multimodal transformer that efficiently models hand-object interactions for action recognition. The authors recognize the limitations of using 3D hand poses alone, as they do not capture objects and environments fully. Instead, HandFormer combines high-temporal-resolution hand poses with sparse RGB frames to encode scene semantics. The model factors hand modeling into short-term trajectories for each joint, resulting in a remarkably efficient and accurate representation. Unimodal HandFormer, using only hand poses, outperforms existing skeleton-based methods at 5x fewer FLOPs. When combined with RGB, HandFormer achieves new state-of-the-art performance on Assembly101 and H2O datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about recognizing actions like assembly or water sports by analyzing how people use their hands and the objects around them. Right now, it’s hard to do this because we don’t have good ways to capture what our hands are doing and the things around us at the same time. The authors created a new tool called HandFormer that can do both things efficiently. It works by looking at how our hands move over short periods of time and combining that with some information from cameras. This allows it to recognize actions more accurately than other methods, even when using less computer power. |
Keywords
* Artificial intelligence * Semantics * Transformer
