Summary of Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-schmidt Independence Criterion, by Yuheng Yang
Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion
by Yuheng Yang
First submitted to arxiv on: 25 Dec 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 This paper proposes a novel approach to human skeleton-based action recognition that overcomes limitations of current methods by explicitly modeling dependencies between any pair of joints. The approach, which includes a dependency refinement method and a framework utilizing the Hilbert-Schmidt Independence Criterion, sets state-of-the-art performance on several benchmark datasets. By leveraging these techniques, the authors aim to improve the accuracy of action recognition systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding better ways to recognize human actions from videos using 3D skeleton data. Right now, most methods only look at how joints are connected, but this doesn’t work well for complex movements. The researchers came up with a new approach that takes into account relationships between any two joints, not just the ones close together. They also developed a way to identify different actions without getting confused by the huge amount of data involved. By doing things this way, they were able to beat current records on three important datasets. |
