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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2411.00883 |
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| _version_ | 1866912102404325376 |
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| author | Chen, Shimin Li, Wei Gu, Jianyang Chen, Chen Guo, Yandong |
| author_facet | Chen, Shimin Li, Wei Gu, Jianyang Chen, Chen Guo, Yandong |
| contents | In the task of temporal action localization of ActivityNet-1.3 datasets, we propose to locate the temporal boundaries of each action and predict action class in untrimmed videos. We first apply VideoSwinTransformer as feature extractor to extract different features. Then we apply a unified network following Faster-TAD to simultaneously obtain proposals and semantic labels. Last, we ensemble the results of different temporal action detection models which complement each other. Faster-TAD simplifies the pipeline of TAD and gets remarkable performance, obtaining comparable results as those of multi-step approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00883 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Technical Report for ActivityNet Challenge 2022 -- Temporal Action Localization Chen, Shimin Li, Wei Gu, Jianyang Chen, Chen Guo, Yandong Computer Vision and Pattern Recognition In the task of temporal action localization of ActivityNet-1.3 datasets, we propose to locate the temporal boundaries of each action and predict action class in untrimmed videos. We first apply VideoSwinTransformer as feature extractor to extract different features. Then we apply a unified network following Faster-TAD to simultaneously obtain proposals and semantic labels. Last, we ensemble the results of different temporal action detection models which complement each other. Faster-TAD simplifies the pipeline of TAD and gets remarkable performance, obtaining comparable results as those of multi-step approaches. |
| title | Technical Report for ActivityNet Challenge 2022 -- Temporal Action Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.00883 |