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Main Authors: Liu, Chang, Ding, Henghui, Ravi, Nikhila, Wei, Yunchao, He, Shuting, Bai, Song, Torr, Philip, Cao, Leilei, Zhang, Jinrong, Miao, Deshui, He, Xusheng, Gong, Dengxian, Wang, Zhiyu, Gao, Mingqi, Hong, Jihwan, Wu, Canyang, Guan, Weili, Wu, Jianlong, Nie, Liqiang, Huang, Xingsen, Gu, Yameng, Yu, Xiaogang, Li, Xin, Yang, Ming-Hsuan, Li, Sijie, Han, Jungong, Niu, Quanzhu, Chen, Shihao, Wu, Yuanzheng, Zhou, Yikang, Zhang, Tao, Yuan, Haobo, Qi, Lu, Ji, Shunping, Yang, Chao, Tian, Chao, Zhu, Guoqing, Yang, Kai, Mo, Zhifan, Zhang, Haijun, Kang, Xudong, Li, Shutao, Do, Jaeyoung
Format: Preprint
Published: 2026
Subjects:
Computer Vision and Pattern Recognition
Online Access:https://arxiv.org/abs/2604.26031
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Table of Contents:
  • This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-the-art models under highly unconstrained conditions. To provide a comprehensive assessment, the 2026 edition features three specialized tracks: the MOSE track for tracking objects within densely cluttered and severely occluded scenarios; the MeViS-Text track for localizing targets via motion-focused linguistic expressions; and the newly inaugurated MeViS-Audio track, which pioneers acoustic-driven object segmentation. By introducing previously unreleased challenging data and analyzing the cutting-edge, multimodal solutions submitted by participants, this report highlights the community's latest technical advancements and charts promising future directions for robust video scene comprehension.

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