Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910509422346240 |
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| author | Mosconi, Matteo Sorokin, Andriy Panariello, Aniello Porrello, Angelo Bonato, Jacopo Cotogni, Marco Sabetta, Luigi Calderara, Simone Cucchiara, Rita |
| author_facet | Mosconi, Matteo Sorokin, Andriy Panariello, Aniello Porrello, Angelo Bonato, Jacopo Cotogni, Marco Sabetta, Luigi Calderara, Simone Cucchiara, Rita |
| contents | The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of Continual Learning is crucial. While numerous studies focus on skeleton-based action recognition from a traditional offline perspective, only a handful venture into online approaches. In this respect, we introduce CHARON (Continual Human Action Recognition On skeletoNs), which maintains consistent performance while operating within an efficient framework. Through techniques like uniform sampling, interpolation, and a memory-efficient training stage based on masking, we achieve improved recognition accuracy while minimizing computational overhead. Our experiments on Split NTU-60 and the proposed Split NTU-120 datasets demonstrate that CHARON sets a new benchmark in this domain. The code is available at https://github.com/Sperimental3/CHARON. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01397 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning Mosconi, Matteo Sorokin, Andriy Panariello, Aniello Porrello, Angelo Bonato, Jacopo Cotogni, Marco Sabetta, Luigi Calderara, Simone Cucchiara, Rita Computer Vision and Pattern Recognition Artificial Intelligence The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of Continual Learning is crucial. While numerous studies focus on skeleton-based action recognition from a traditional offline perspective, only a handful venture into online approaches. In this respect, we introduce CHARON (Continual Human Action Recognition On skeletoNs), which maintains consistent performance while operating within an efficient framework. Through techniques like uniform sampling, interpolation, and a memory-efficient training stage based on masking, we achieve improved recognition accuracy while minimizing computational overhead. Our experiments on Split NTU-60 and the proposed Split NTU-120 datasets demonstrate that CHARON sets a new benchmark in this domain. The code is available at https://github.com/Sperimental3/CHARON. |
| title | Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2407.01397 |