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

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.