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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2510.11063 |
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| _version_ | 1866912644337762304 |
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| author | Liu, Chang Ding, Henghui Ying, Kaining Hong, Lingyi Xu, Ning Yang, Linjie Fan, Yuchen Gao, Mingqi Chen, Jingkun Miao, Yunqi Wu, Gengshen Qin, Zhijin Han, Jungong Zhang, Zhixiong Ding, Shuangrui Dong, Xiaoyi Zang, Yuhang Cao, Yuhang Wang, Jiaqi Lim, Chang Soo Moon, Joonyoung Cho, Donghyeon Li, Tingmin Li, Yixuan Yang, Yang Yan, An Cao, Leilei Lu, Feng Hong, Ran Jiang, Youhai Zhu, Fengjie Xie, Yujie Zhang, Hongyang Liu, Zhihui Ruan, Shihai Niu, Quanzhu Gong, Dengxian Chen, Shihao Zhang, Tao Zhou, Yikang Yuan, Haobo Qi, Lu Li, Xiangtai Ji, Shunping Hong, Ran Lu, Feng Cao, Leilei Yan, An Nekrasov, Alexey Athar, Ali de Geus, Daan Hermans, Alexander Leibe, Bastian |
| author_facet | Liu, Chang Ding, Henghui Ying, Kaining Hong, Lingyi Xu, Ning Yang, Linjie Fan, Yuchen Gao, Mingqi Chen, Jingkun Miao, Yunqi Wu, Gengshen Qin, Zhijin Han, Jungong Zhang, Zhixiong Ding, Shuangrui Dong, Xiaoyi Zang, Yuhang Cao, Yuhang Wang, Jiaqi Lim, Chang Soo Moon, Joonyoung Cho, Donghyeon Li, Tingmin Li, Yixuan Yang, Yang Yan, An Cao, Leilei Lu, Feng Hong, Ran Jiang, Youhai Zhu, Fengjie Xie, Yujie Zhang, Hongyang Liu, Zhihui Ruan, Shihai Niu, Quanzhu Gong, Dengxian Chen, Shihao Zhang, Tao Zhou, Yikang Yuan, Haobo Qi, Lu Li, Xiangtai Ji, Shunping Hong, Ran Lu, Feng Cao, Leilei Yan, An Nekrasov, Alexey Athar, Ali de Geus, Daan Hermans, Alexander Leibe, Bastian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11063 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation Liu, Chang Ding, Henghui Ying, Kaining Hong, Lingyi Xu, Ning Yang, Linjie Fan, Yuchen Gao, Mingqi Chen, Jingkun Miao, Yunqi Wu, Gengshen Qin, Zhijin Han, Jungong Zhang, Zhixiong Ding, Shuangrui Dong, Xiaoyi Zang, Yuhang Cao, Yuhang Wang, Jiaqi Lim, Chang Soo Moon, Joonyoung Cho, Donghyeon Li, Tingmin Li, Yixuan Yang, Yang Yan, An Cao, Leilei Lu, Feng Hong, Ran Jiang, Youhai Zhu, Fengjie Xie, Yujie Zhang, Hongyang Liu, Zhihui Ruan, Shihai Niu, Quanzhu Gong, Dengxian Chen, Shihao Zhang, Tao Zhou, Yikang Yuan, Haobo Qi, Lu Li, Xiangtai Ji, Shunping Hong, Ran Lu, Feng Cao, Leilei Yan, An Nekrasov, Alexey Athar, Ali de Geus, Daan Hermans, Alexander Leibe, Bastian Computer Vision and Pattern Recognition 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. |
| title | LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.11063 |