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

Henghui Ding, Lingyi Hong, Chang Liu, Ning Xu, Linjie Yang +28 more
9/9/2024
cs.CV

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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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Cite this paper

@article{ding2024lsvos,
  title  = {LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation},
  author = {Henghui Ding and Lingyi Hong and Chang Liu and Ning Xu and Linjie Yang and Yuchen Fan and Deshui Miao and Yameng Gu and Xin Li and Zhenyu He and Yaowei Wang and Ming-Hsuan Yang and Jinming Chai and Qin Ma and Junpei Zhang and Licheng Jiao and Fang Liu and Xinyu Liu and Jing Zhang and Kexin Zhang and Xu Liu and LingLing Li and Hao Fang and Feiyu Pan and Xiankai Lu and Wei Zhang and Runmin Cong and Tuyen Tran and Bin Cao and Yisi Zhang and Hanyi Wang and Xingjian He and Jing Liu},
  year   = {2024},
  eprint = {2409.05847},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.05847v1}
}

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