The 6th LSVOS challenge will be held in conjunction with ECCV 2024 in MiCo Milano. In this edition of the workshop and challenge, we replace the classic YouTube-VOS benchmark with MOSE and LVOS to study the VOS under more challenging complex environments. MOSE focuses on complex scenes, including the disappearance-reappearance of objects, inconspicuous small objects, heavy occlusions, crowded environments, etc. LVOS focuses on long-term videos, with complex object motion and long-term reappearance. Besides, we also replace the origin YouTube-RVOS benchmark with MeViS. MeViS focuses on referring the target object in a video through its motion descriptions instead of static attributes, which breaks the basic design principles behind existing RVOS methods and boosts the rethinking of motion modeling. In addition, we will hold a series of talks by the leading experts in video understating.

The 6th LSVOS challenge includes two tracks. In this year, we replace the classic YouTube-VOS benchmark with MOSE and LVOS to study the VOS under more challenging complex environments. Besides, we also replace the origin YouTube-RVOS benchmark with MeViS.

Below are the links and task descriptions for the two tracks:

The final technique report for the two tracks are available now! Please refer to this link.

Track 1: Video Object Segmentation (VOS)

Rank	Team Name	Team Members	Affiliation	J & F | J | F	Tech Report
1st	PCL VisionLab	Deshui Miao1,2, Yameng Gu1, Xin Li2, Zhenyu He1,2, Yaowei Wang2, Ming-Hsuan Yang3	1 Harbin Institute of Technology, Shenzhen 2 Peng Cheng Laboratory 3 University of California at Merced	80.90 | 76.16 | 85.63	PDF
2nd	yuanjie	Jinming Chai, Qin Ma, Junpei Zhang, Licheng Jiao, Fang Liu	Intelligent Perception and Image Understanding Lab, Xidian University	80.84 | 76.42 | 85.26	PDF
3rd	Xy-unu	Xinyu Liu, Jing Zhang, Kexin Zhang, Xu Liu, LingLing Li	Intelligent Perception and Image Understanding Lab, Xidian University	79.52 | 75.16 | 83.88	PDF
4th	Sch89.89	Cannot be reached.	Cannot be reached.	76.35 | 71.94 | 80.76
4th	MVP-TIME	Feiyu Pan, Hao Fang, Runmin Cong, Wei Zhang, Xiankai Lu	Shandong University	75.79 | 71.25 | 80.33	PDF

Track 2: Referring Video Object Segmentation (RVOS)

Rank	Team Name	Team Members	Affiliation	J & F | J | F	Tech Report
1st	MVP-TIME	Hao Fang, Feiyu Pan, Xiankai Lu, Wei Zhang, Runmin Cong	Shandong University	62.57 | 58.98 | 66.15	PDF
2nd	TXT	Tuyen Tran	Applied Artificial Intelligence Institute, Deakin University, Australia	60.40 | 57.02 | 63.78	PDF
3rd	CASIA_IVA	Bin Cao1,2,3, Yisi Zhang4, Hanyi Wang2, Xingjian He1, Jing Liu1,2	1 Institute of Automation, Chinese Academy of Sciences, 2 School of Artificial Intelligence, University of Chinese Academy of Sciences, 3 Beijing Academy of Artificial Intelligence 4 University of Science and Technology Beijing	60.36 | 56.88 | 63.85	PDF