Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.26031 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917444891705344 |
|---|---|
| author | 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 |
| author_facet | 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 |
| 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26031 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding 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 Computer Vision and Pattern Recognition 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. |
| title | Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.26031 |