A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917836352389120 |
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| author | Dwibedi, Debidatta Aytar, Yusuf Tompson, Jonathan Sermanet, Pierre Zisserman, Andrew |
| author_facet | Dwibedi, Debidatta Aytar, Yusuf Tompson, Jonathan Sermanet, Pierre Zisserman, Andrew |
| contents | We discuss some consistent issues on how RepNet has been evaluated in various papers. As a way to mitigate these issues, we report RepNet performance results on different datasets, and release evaluation code and the RepNet checkpoint to obtain these results. Code URL: https://github.com/google-research/google-research/blob/master/repnet/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08878 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos Dwibedi, Debidatta Aytar, Yusuf Tompson, Jonathan Sermanet, Pierre Zisserman, Andrew Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We discuss some consistent issues on how RepNet has been evaluated in various papers. As a way to mitigate these issues, we report RepNet performance results on different datasets, and release evaluation code and the RepNet checkpoint to obtain these results. Code URL: https://github.com/google-research/google-research/blob/master/repnet/ |
| title | A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.08878 |