PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of
  Complex Videos in the Wild
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/.
67ff25b765b52d1b69c1f6c1
Henghui Ding
extracted_pending
2025-04-16T03:55:17.825Z
67ff29ecbf6889a333c69c7a
https://cdn-avatars.huggingface.co/v1/production/uploads/67ff29ecbf6889a333c69c7a/zilMQrxIgUKYvHBVCHaKL.jpeg
Henghui Ding
user
HenghuiDing
67ff25b765b52d1b69c1f6c2
Chang Liu
67ff25b765b52d1b69c1f6c3
Nikhila Ravi
67ff25b765b52d1b69c1f6c4
Shuting He
67ff25b765b52d1b69c1f6c5
Yunchao Wei
67ff25b765b52d1b69c1f6c6
Song Bai
67ff25b765b52d1b69c1f6c7
Philip Torr
67ff25b765b52d1b69c1f6c8
Kehuan Song
67ff25b765b52d1b69c1f6c9
Xinglin Xie
67ff25b765b52d1b69c1f6ca
Kexin Zhang
67ff25b765b52d1b69c1f6cb
Licheng Jiao
67ff25b765b52d1b69c1f6cc
Lingling Li
67ff25b765b52d1b69c1f6cd
Shuyuan Yang
67ff25b765b52d1b69c1f6ce
Xuqiang Cao
67ff25b765b52d1b69c1f6cf
Linnan Zhao
67ff25b765b52d1b69c1f6d0
Jiaxuan Zhao
67ff25b765b52d1b69c1f6d1
Fang Liu
67ff25b765b52d1b69c1f6d2
Mengjiao Wang
67ff25b765b52d1b69c1f6d3
Junpei Zhang
67ff25b765b52d1b69c1f6d4
Xu Liu
67ff25b765b52d1b69c1f6d5
Yuting Yang
67ff25b765b52d1b69c1f6d6
Mengru Ma
67ff25b765b52d1b69c1f6d7
Hao Fang
67ff25b765b52d1b69c1f6d8
Runmin Cong
67ff25b765b52d1b69c1f6d9
Xiankai Lu
67ff25b765b52d1b69c1f6da
Zhiyang Che
67ff25b765b52d1b69c1f6db
Wei Zhan
67ff25b765b52d1b69c1f6dc
Tianming Liang
67ff25b765b52d1b69c1f6dd
Haichao Jiang
67ff25b765b52d1b69c1f6de
Wei-Shi Zheng
67ff25b765b52d1b69c1f6df
Jian-Fang Hu
67ff25b765b52d1b69c1f6e0
Haobo Yuan
67ff25b765b52d1b69c1f6e1
Xiangtai Li
claimed_verified
2025-04-16T08:42:58.429Z
63958b4414513eaf9029ebf1
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/U1g5H071pWRswGAG9UTpo.png
Xiangtai Li
user
LXT
67ff25b765b52d1b69c1f6e2
Tao Zhang
67ff25b765b52d1b69c1f6e3
Lu Qi
67ff25b765b52d1b69c1f6e4
Ming-Hsuan Yang
PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of
  Complex Videos in the Wild
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/.
The PVUW Challenge provides insights into complex video segmentation by evaluating methodologies on challenging datasets in two tracks: MOSE and MeViS.
complex scene video object segmentation motion-guided language-based video segmentation