Technical Report for SoccerNet Challenge 2022 -- Replay Grounding Task
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866915002560020480 |
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| author | Chen, Shimin Li, Wei Chu, Jiaming Chen, Chen Zhang, Chen Guo, Yandong |
| author_facet | Chen, Shimin Li, Wei Chu, Jiaming Chen, Chen Zhang, Chen Guo, Yandong |
| contents | In order to make full use of video information, we transform the replay grounding problem into a video action location problem. We apply a unified network Faster-TAD proposed by us for temporal action detection to get the results of replay grounding. Finally, by observing the data distribution of the training data, we refine the output of the model to get the final submission. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00881 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Technical Report for SoccerNet Challenge 2022 -- Replay Grounding Task Chen, Shimin Li, Wei Chu, Jiaming Chen, Chen Zhang, Chen Guo, Yandong Computer Vision and Pattern Recognition In order to make full use of video information, we transform the replay grounding problem into a video action location problem. We apply a unified network Faster-TAD proposed by us for temporal action detection to get the results of replay grounding. Finally, by observing the data distribution of the training data, we refine the output of the model to get the final submission. |
| title | Technical Report for SoccerNet Challenge 2022 -- Replay Grounding Task |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.00881 |