LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
This is an incremental update to a domain-specific challenge for video segmentation researchers, focusing on more realistic and complex environments.
The paper introduces the 6th LSVOS challenge, which replaces older benchmarks with new datasets (MOSE, LVOS, MeViS) to evaluate video object segmentation in complex scenes, attracting 129 teams from over 20 institutes.
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/.