Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track
<|reference_start|>Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track: Video Object Segmentation (VOS) task aims to segmenting a particular object instance throughout the entire video sequence given only the object mask of the first frame. Recently, Segment Anything Model 2 (SAM 2) is proposed, which is a foundation model towards solving promptable visual segmentation in images and videos. SAM 2 builds a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. SAM 2 is a simple transformer architecture with streaming memory for real-time video processing, which trained on the date provides strong performance across a wide range of tasks. In this work, we evaluate the zero-shot performance of SAM 2 on the more challenging VOS datasets MOSE and LVOS. Without fine-tuning on the training set, SAM 2 achieved 75.79 J&F on the test set and ranked 4th place for 6th LSVOS Challenge VOS Track.<|reference_end|>
@article{pan2024video,
    title={Video Object Segmentation via SAM 2: The 4th Solution for LSVOS
  Challenge VOS Track},
    author={Feiyu Pan, Hao Fang, Runmin Cong, Wei Zhang, Xiankai Lu},
    journal={arXiv preprint arXiv:2408.10125},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10125},
    primaryClass={cs.CV}
}