Summary of Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning, by Matteo Mosconi et al.
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
by Matteo Mosconi, Andriy Sorokin, Aniello Panariello, Angelo Porrello, Jacopo Bonato, Marco Cotogni, Luigi Sabetta, Simone Calderara, Rita Cucchiara
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 CHARON, a deep learning model that efficiently recognizes human actions using skeletal data. By exploring Continual Learning, the authors aim to improve action recognition while minimizing computational overhead. They achieve this through techniques like uniform sampling, interpolation, and memory-efficient training based on masking. The experiments on Split NTU-60 and Split NTU-120 datasets demonstrate CHARON’s effectiveness in setting a new benchmark for skeleton-based action recognition. This work is particularly relevant in the context of online approaches to action recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn to recognize human actions, like dancing or walking, using special data from skeletons. The authors want to make this process more efficient and accurate while still being fast. They do this by using some clever techniques that make it easier for the computer to learn without needing too many resources. They tested their method on two big datasets and showed that it works really well. This is important because it can help us develop better machines that can recognize and understand human actions. |
Keywords
» Artificial intelligence » Continual learning » Deep learning
