PVUW 2025 挑戰賽報告：複雜野外場景中像素級視頻理解的新進展

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

本報告全面概述了與CVPR 2025同期舉辦的第四屆野外像素級視頻理解挑戰賽（PVUW）。報告總結了挑戰賽的成果、參與方法以及未來研究方向。本次挑戰賽設有兩個賽道：MOSE，專注於複雜場景視頻對象分割；MeViS，針對基於運動和語言的視頻分割。兩個賽道均引入了新的、更具挑戰性的數據集，旨在更好地反映現實世界場景。通過詳細的評估與分析，本次挑戰賽為複雜視頻分割領域的當前最新技術與新興趨勢提供了寶貴的見解。更多信息可訪問研討會網站：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/.