LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916386437070848 |
|---|---|
| author | Ding, Henghui Hong, Lingyi Liu, Chang Xu, Ning Yang, Linjie Fan, Yuchen Miao, Deshui Gu, Yameng Li, Xin He, Zhenyu Wang, Yaowei Yang, Ming-Hsuan Chai, Jinming Ma, Qin Zhang, Junpei Jiao, Licheng Liu, Fang Liu, Xinyu Zhang, Jing Zhang, Kexin Liu, Xu Li, LingLing Fang, Hao Pan, Feiyu Lu, Xiankai Zhang, Wei Cong, Runmin Tran, Tuyen Cao, Bin Zhang, Yisi Wang, Hanyi He, Xingjian Liu, Jing |
| author_facet | Ding, Henghui Hong, Lingyi Liu, Chang Xu, Ning Yang, Linjie Fan, Yuchen Miao, Deshui Gu, Yameng Li, Xin He, Zhenyu Wang, Yaowei Yang, Ming-Hsuan Chai, Jinming Ma, Qin Zhang, Junpei Jiao, Licheng Liu, Fang Liu, Xinyu Zhang, Jing Zhang, Kexin Liu, Xu Li, LingLing Fang, Hao Pan, Feiyu Lu, Xiankai Zhang, Wei Cong, Runmin Tran, Tuyen Cao, Bin Zhang, Yisi Wang, Hanyi He, Xingjian Liu, Jing |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation Ding, Henghui Hong, Lingyi Liu, Chang Xu, Ning Yang, Linjie Fan, Yuchen Miao, Deshui Gu, Yameng Li, Xin He, Zhenyu Wang, Yaowei Yang, Ming-Hsuan Chai, Jinming Ma, Qin Zhang, Junpei Jiao, Licheng Liu, Fang Liu, Xinyu Zhang, Jing Zhang, Kexin Liu, Xu Li, LingLing Fang, Hao Pan, Feiyu Lu, Xiankai Zhang, Wei Cong, Runmin Tran, Tuyen Cao, Bin Zhang, Yisi Wang, Hanyi He, Xingjian Liu, Jing Computer Vision and Pattern Recognition 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/. |
| title | LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.05847 |