Summary of Chase: Learning Convex Hull Adaptive Shift For Skeleton-based Multi-entity Action Recognition, by Yuhang Wen et al.
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
by Yuhang Wen, Mengyuan Liu, Songtao Wu, Beichen Ding
First submitted to arxiv on: 9 Oct 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 introduces a novel approach to skeleton-based multi-entity action recognition, which addresses the limitations of existing models in recognizing group activities involving diverse entities. The proposed Convex Hull Adaptive Shift (CHASE) method mitigates inter-entity distribution gaps and biases subsequent backbones, allowing for improved performance in multi-entity scenarios. CHASE comprises a learnable parameterized network with two key components: Implicit Convex Hull Constrained Adaptive Shift and Coefficient Learning Block. Additionally, the paper proposes Mini-batch Pair-wise Maximum Mean Discrepancy as an auxiliary objective to guide optimization. Extensive experiments on six datasets demonstrate the effectiveness of CHASE in adapting to single-entity backbones and boosting their performance in multi-entity scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to recognize actions that involve multiple people or objects. Right now, computers are not very good at this because they don’t understand how different people or objects move together. The researchers created a new method called CHASE that helps computers better understand these interactions. CHASE uses special algorithms to make sure the computer is looking at all the right things and isn’t biased towards one type of action over another. This makes it much better at recognizing actions that involve multiple entities. |
Keywords
» Artificial intelligence » Boosting » Optimization
