Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915012829773824 |
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| author | Mehmood, Faisal Guo, Xin Chen, Enqing Akbar, Muhammad Azeem Khan, Arif Ali Ullah, Sami |
| author_facet | Mehmood, Faisal Guo, Xin Chen, Enqing Akbar, Muhammad Azeem Khan, Arif Ali Ullah, Sami |
| contents | Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06553 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR) Mehmood, Faisal Guo, Xin Chen, Enqing Akbar, Muhammad Azeem Khan, Arif Ali Ullah, Sami Computer Vision and Pattern Recognition Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. |
| title | Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR) |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.06553 |