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Détails bibliographiques
Auteurs principaux: Ding, Henghui, Liu, Chang, Ravi, Nikhila, He, Shuting, Wei, Yunchao, Bai, Song, Torr, Philip, Song, Kehuan, Xie, Xinglin, Zhang, Kexin, Jiao, Licheng, Li, Lingling, Yang, Shuyuan, Cao, Xuqiang, Zhao, Linnan, Zhao, Jiaxuan, Liu, Fang, Wang, Mengjiao, Zhang, Junpei, Liu, Xu, Yang, Yuting, Ma, Mengru, Fang, Hao, Cong, Runmin, Lu, Xiankai, Chen, Zhiyang, Zhang, Wei, Liang, Tianming, Jiang, Haichao, Zheng, Wei-Shi, Hu, Jian-Fang, Yuan, Haobo, Li, Xiangtai, Zhang, Tao, Qi, Lu, Yang, Ming-Hsuan
Format: Preprint
Publié: 2025
Sujets:
Computer Vision and Pattern Recognition
Accès en ligne:https://arxiv.org/abs/2504.11326
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  • 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/.

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