LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation

• URL: http://arxiv.org/abs/2409.05847v1
• Date: Mon, 9 Sep 2024 17:45:45 GMT
• Title: LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
• Authors: Henghui Ding, Lingyi Hong, Chang Liu, Ning Xu, Linjie Yang, Yuchen Fan, Deshui Miao, Yameng Gu, Xin Li, Zhenyu He, Yaowei Wang, Ming-Hsuan Yang, Jinming Chai, Qin Ma, Junpei Zhang, Licheng Jiao, Fang Liu, Xinyu Liu, Jing Zhang, Kexin Zhang, Xu Liu, LingLing Li, Hao Fang, Feiyu Pan, Xiankai Lu, Wei Zhang, Runmin Cong, Tuyen Tran, Bin Cao, Yisi Zhang, Hanyi Wang, Xingjian He, Jing Liu,
• Abstract summary: This paper introduces the 6th Large-scale Video Object (LSVOS) challenge in conjunction with ECCV 2024 workshop. This year's challenge includes two tasks: Video Object (VOS) and Referring Video Object (RVOS) This year's challenge attracted 129 registered teams from more than 20 institutes across over 8 countries.
• Score: 124.50550604020684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes. In this paper, we introduce the 6th Large-scale Video Object Segmentation (LSVOS) challenge in conjunction with ECCV 2024 workshop. This year's challenge includes two tasks: Video Object Segmentation (VOS) and Referring Video Object Segmentation (RVOS). In this year, we replace the classic YouTube-VOS and YouTube-RVOS benchmark with latest datasets MOSE, LVOS, and MeViS to assess VOS under more challenging complex environments. This year's challenge attracted 129 registered teams from more than 20 institutes across over 8 countries. This report include the challenge and dataset introduction, and the methods used by top 7 teams in two tracks. More details can be found in our homepage https://lsvos.github.io/.

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