OpenIXCLab/SeC-4B

SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction

[📂 GitHub] [📦 Benchmark] [🌐 Homepage] [📄 Paper]

Highlights

• 🔥We introduce Segment Concept (SeC), a concept-driven segmentation framework for video object segmentation that integrates Large Vision-Language Models (LVLMs) for robust, object-centric representations.
• 🔥SeC dynamically balances semantic reasoning with feature matching, adaptively adjusting computational efforts based on scene complexity for optimal segmentation performance.
• 🔥We propose the Semantic Complex Scenarios Video Object Segmentation (SeCVOS) benchmark, designed to evaluate segmentation in challenging scenarios.

SeC Performance

Model	SA-V val	SA-V test	LVOS v2 val	MOSE val	DAVIS 2017 val	YTVOS 2019 val	SeCVOS
SAM 2.1	78.6	79.6	84.1	74.5	90.6	88.7	58.2
SAMURAI	79.8	80.0	84.2	72.6	89.9	88.3	62.2
SAM2.1Long	81.1	81.2	85.9	75.2	91.4	88.7	62.3
SeC (Ours)	82.7	81.7	86.5	75.3	91.3	88.6	70.0

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Citation

If you find this project useful in your research, please consider citing:

@article{zhang2025sec,
  title     = {SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction},
  author    = {Zhixiong Zhang and Shuangrui Ding and Xiaoyi Dong and Songxin He and Jianfan Lin and Junsong Tang and Yuhang Zang and Yuhang Cao and Dahua Lin and Jiaqi Wang},
  journal   = {arXiv preprint arXiv:2507.15852},
  year      = {2025}
}