LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation

• URL: http://arxiv.org/abs/2510.11063v1
• Date: Mon, 13 Oct 2025 07:02:09 GMT
• Title: LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation
• Authors: Chang Liu, Henghui Ding, Kaining Ying, Lingyi Hong, Ning Xu, Linjie Yang, Yuchen Fan, Mingqi Gao, Jingkun Chen, Yunqi Miao, Gengshen Wu, Zhijin Qin, Jungong Han, Zhixiong Zhang, Shuangrui Ding, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Jiaqi Wang, Chang Soo Lim, Joonyoung Moon, Donghyeon Cho, Tingmin Li, Yixuan Li, Yang Yang, An Yan, Leilei Cao, Feng Lu, Ran Hong, Youhai Jiang, Fengjie Zhu, Yujie Xie, Hongyang Zhang, Zhihui Liu, Shihai Ruan, Quanzhu Niu, Dengxian Gong, Shihao Chen, Tao Zhang, Yikang Zhou, Haobo Yuan, Lu Qi, Xiangtai Li, Shunping Ji, Ran Hong, Feng Lu, Leilei Cao, An Yan, Alexey Nekrasov, Ali Athar, Daan de Geus, Alexander Hermans, Bastian Leibe,
• Abstract summary: This report presents an overview of the 7th Large-scale Video Object (LSVOS) Challenge held in conjunction with ICCV 2025.<n>The 2025 edition features a newly introduced track, Complex VOS (MOSEv2)<n>We summarize datasets and protocols, highlight top-performing solutions, and distill emerging trends.
• Score: 186.14566815158506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This report presents an overview of the 7th Large-scale Video Object Segmentation (LSVOS) Challenge held in conjunction with ICCV 2025. Besides the two traditional tracks of LSVOS that jointly target robustness in realistic video scenarios: Classic VOS (VOS), and Referring VOS (RVOS), the 2025 edition features a newly introduced track, Complex VOS (MOSEv2). Building upon prior insights, MOSEv2 substantially increases difficulty, introducing more challenging but realistic scenarios including denser small objects, frequent disappear/reappear events, severe occlusions, adverse weather and lighting, etc., pushing long-term consistency and generalization beyond curated benchmarks. The challenge retains standard ${J}$, $F$, and ${J\&F}$ metrics for VOS and RVOS, while MOSEv2 adopts ${J\&\dot{F}}$ as the primary ranking metric to better evaluate objects across scales and disappearance cases. We summarize datasets and protocols, highlight top-performing solutions, and distill emerging trends, such as the growing role of LLM/MLLM components and memory-aware propagation, aiming to chart future directions for resilient, language-aware video segmentation in the wild.

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