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

Authors :
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
Publication Year :
2024

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/.<br />Comment: ECCV 2024 LSVOS Challenge Report: https://lsvos.github.io/

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2409.05847
Document Type :
Working Paper