LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS
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Abstract (arXiv)
Video Object Segmentation (VOS) presents several challenges, including object occlusion and fragmentation, the dis-appearance and re-appearance of objects, and tracking specific objects within crowded scenes. In this work, we combine the strengths of the state-of-the-art (SOTA) models SAM2 and Cutie to address these challenges. Additionally, we explore the impact of various hyperparameters on video instance segmentation performance. Our approach achieves a J&F score of 0.7952 in the testing phase of LSVOS challenge VOS track, ranking third overall.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 8 references that are in the map. First posted on arXiv 20 Aug 2024.
Builds on
8 of 8 references.
References · 8
- 2023Segment Anythingcited by 8,234
- 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
- 2022XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Modelcited by 439
- 2023MOSE: A New Dataset for Video Object Segmentation in Complex Scenescited by 234
- 2023MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressionscited by 228
- 2023Putting the Object Back into Video Object Segmentationcited by 188
- 2022LVOS: A Benchmark for Long-term Video Object Segmentationcited by 77
- 2024LVOS: A Benchmark for Large-scale Long-term Video Object Segmentationcited by 44
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.