UNINEXT-Cutie: The 1st Solution for LSVOS Challenge RVOS Track
<|reference_start|>UNINEXT-Cutie: The 1st Solution for LSVOS Challenge RVOS Track: Referring video object segmentation (RVOS) relies on natural language expressions to segment target objects in video. In this year, LSVOS Challenge RVOS Track replaced 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, posing a greater challenge to RVOS task. In this work, we integrate strengths of that leading RVOS and VOS models to build up a simple and effective pipeline for RVOS. Firstly, We finetune the state-of-the-art RVOS model to obtain mask sequences that are correlated with language descriptions. Secondly, based on a reliable and high-quality key frames, we leverage VOS model to enhance the quality and temporal consistency of the mask results. Finally, we further improve the performance of the RVOS model using semi-supervised learning. Our solution achieved 62.57 J&F on the MeViS test set and ranked 1st place for 6th LSVOS Challenge RVOS Track.<|reference_end|>
@article{fang2024uninext-cutie:,
    title={UNINEXT-Cutie: The 1st Solution for LSVOS Challenge RVOS Track},
    author={Hao Fang, Feiyu Pan, Xiankai Lu, Wei Zhang, Runmin Cong},
    journal={arXiv preprint arXiv:2408.10129},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10129},
    primaryClass={cs.CV}
}