LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS
<|reference_start|>LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS: 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.<|reference_end|>
@article{liu2024lsvos,
    title={LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS},
    author={Xinyu Liu, Jing Zhang, Kexin Zhang, Xu Liu, Lingling Li},
    journal={arXiv preprint arXiv:2408.10469},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10469},
    primaryClass={cs.CV cs.IR}
}