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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.11326 |
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| _version_ | 1866910914986377216 |
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| author | Ding, Henghui Liu, Chang Ravi, Nikhila He, Shuting Wei, Yunchao Bai, Song Torr, Philip Song, Kehuan Xie, Xinglin Zhang, Kexin Jiao, Licheng Li, Lingling Yang, Shuyuan Cao, Xuqiang Zhao, Linnan Zhao, Jiaxuan Liu, Fang Wang, Mengjiao Zhang, Junpei Liu, Xu Yang, Yuting Ma, Mengru Fang, Hao Cong, Runmin Lu, Xiankai Chen, Zhiyang Zhang, Wei Liang, Tianming Jiang, Haichao Zheng, Wei-Shi Hu, Jian-Fang Yuan, Haobo Li, Xiangtai Zhang, Tao Qi, Lu Yang, Ming-Hsuan |
| author_facet | Ding, Henghui Liu, Chang Ravi, Nikhila He, Shuting Wei, Yunchao Bai, Song Torr, Philip Song, Kehuan Xie, Xinglin Zhang, Kexin Jiao, Licheng Li, Lingling Yang, Shuyuan Cao, Xuqiang Zhao, Linnan Zhao, Jiaxuan Liu, Fang Wang, Mengjiao Zhang, Junpei Liu, Xu Yang, Yuting Ma, Mengru Fang, Hao Cong, Runmin Lu, Xiankai Chen, Zhiyang Zhang, Wei Liang, Tianming Jiang, Haichao Zheng, Wei-Shi Hu, Jian-Fang Yuan, Haobo Li, Xiangtai Zhang, Tao Qi, Lu Yang, Ming-Hsuan |
| contents | This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challenge outcomes, participating methodologies, and future research directions. The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation. Both tracks introduce new, more challenging datasets designed to better reflect real-world scenarios. Through detailed evaluation and analysis, the challenge offers valuable insights into the current state-of-the-art and emerging trends in complex video segmentation. More information can be found on the workshop website: https://pvuw.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11326 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild Ding, Henghui Liu, Chang Ravi, Nikhila He, Shuting Wei, Yunchao Bai, Song Torr, Philip Song, Kehuan Xie, Xinglin Zhang, Kexin Jiao, Licheng Li, Lingling Yang, Shuyuan Cao, Xuqiang Zhao, Linnan Zhao, Jiaxuan Liu, Fang Wang, Mengjiao Zhang, Junpei Liu, Xu Yang, Yuting Ma, Mengru Fang, Hao Cong, Runmin Lu, Xiankai Chen, Zhiyang Zhang, Wei Liang, Tianming Jiang, Haichao Zheng, Wei-Shi Hu, Jian-Fang Yuan, Haobo Li, Xiangtai Zhang, Tao Qi, Lu Yang, Ming-Hsuan Computer Vision and Pattern Recognition This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challenge outcomes, participating methodologies, and future research directions. The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation. Both tracks introduce new, more challenging datasets designed to better reflect real-world scenarios. Through detailed evaluation and analysis, the challenge offers valuable insights into the current state-of-the-art and emerging trends in complex video segmentation. More information can be found on the workshop website: https://pvuw.github.io/. |
| title | PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.11326 |