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arXiv · 2408.10469

LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS

Xinyu Liu·Jing Zhang·Kexin Zhang·Xu Liu·Lingling Li
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Abstract

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.

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Xinyu Liu, Jing Zhang, Kexin Zhang, Xu Liu, Lingling Li. 2024-08-20. LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS. https://arxiv.org/abs/2408.10469

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