PVUW 2025挑战赛报告：复杂野外场景视频像素级理解的新进展

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

April 15, 2025

作者: 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 Che, Wei Zhan, Tianming Liang, Haichao Jiang, Wei-Shi Zheng, Jian-Fang Hu, Haobo Yuan, Xiangtai Li, Tao Zhang, Lu Qi, Ming-Hsuan Yang

cs.AI

摘要

本报告全面概述了与CVPR 2025同期举办的第四届野外像素级视频理解挑战赛（PVUW）。报告总结了挑战赛的成果、参与方法及未来研究方向。本次挑战赛设有两个赛道：MOSE专注于复杂场景下的视频对象分割，而MeViS则致力于基于运动引导和语言的视频分割。两个赛道均引入了全新且更具挑战性的数据集，旨在更好地反映现实世界场景。通过细致的评估与分析，本次挑战赛为复杂视频分割领域的最新技术和新兴趋势提供了宝贵的洞见。更多信息可访问研讨会官网：https://pvuw.github.io/。

English

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/.