PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild

• URL: http://arxiv.org/abs/2504.11326v2
• Date: Mon, 21 Apr 2025 07:23:45 GMT
• Title: PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild
• Authors: Henghui Ding, Chang Liu, Nikhila Ravi, Shuting He, Yunchao Wei, Song Bai, Philip Torr, Kehuan Song, Xinglin Xie, Kexin Zhang, Licheng Jiao, Lingling Li, Shuyuan Yang, Xuqiang Cao, Linnan Zhao, Jiaxuan Zhao, Fang Liu, Mengjiao Wang, Junpei Zhang, Xu Liu, Yuting Yang, Mengru Ma, Hao Fang, Runmin Cong, Xiankai Lu, Zhiyang Chen, Wei Zhang, Tianming Liang, Haichao Jiang, Wei-Shi Zheng, Jian-Fang Hu, Haobo Yuan, Xiangtai Li, Tao Zhang, Lu Qi, Ming-Hsuan Yang,
• Abstract summary: This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025.<n>The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation.
• Score: 164.8093566483583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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