第六届大规模视频对象分割挑战赛发布新数据集，评估复杂场景下的分割能力。
LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
- 采用MOSE、LVOS、MeViS等新数据集替代旧基准
- 吸引来自8个国家20多所机构的129支队伍参与
- 展示顶尖团队在复杂长视频分割中的方法与成果
尽管当前视频分割模型在现有基准上表现良好，但在复杂场景下仍存在挑战。本文结合ECCV 2024研讨会，推出第六届大规模视频对象分割（LSVOS）挑战赛。今年挑战赛包含两个任务：视频对象分割（VOS）与指代视频对象分割（RVOS）。为评估模型在更复杂环境下的性能，我们以最新的MOSE、LVOS和MeViS数据集取代传统的YouTube-VOS和YouTube-RVOS基准。本届挑战赛吸引了来自超过8个国家、20多所机构的129支注册团队参与。本报告介绍挑战赛与数据集背景，并总结两个赛道中排名前7团队所使用的方法。更多详情请访问官网：https://lsvos.github.io/。
原文摘要 · Abstract (English)
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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