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---
configs:
- config_name: default
data_files:
- split: test
path: viewer.jsonl
license: cc-by-nc-sa-4.0
tags:
- Video
- Segmentation
size_categories:
- n<1K
---
# SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction
[\[📂 GitHub\]](https://github.com/OpenIXCLab/SeC)
[\[📦 Model\]](https://huggingface.co/OpenIXCLab/SeC-4B)
[\[🌐 Homepage\]](https://rookiexiong7.github.io/projects/SeC/)
[\[📄 Paper\]](https://arxiv.org/abs/2507.15852)
## 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.
## SeCVOS Benchmark
We propose the Semantic Complex Scenarios Video Object Segmentation (SeCVOS) benchmark, specifically designed to assess a model’s ability to perform high-level semantic reasoning across complex visual narratives. SeCVOS contains 160 carefully curated multi-shot videos characterized by: 1) Highly discontinuous frame sequences, 2) Frequent reappearance of objects across disparate scenes, and 3) Abrupt shot transitions and dynamic camera motion.
| Benchmark | #Videos | Avg. Duration (s) | Disapp. Rate | Avg. #Scene |
| :------------------------------ | :------: | :---------------: | :----------: | :-----------: |
| DAVIS | 90 | 2.87 | 16.1% | 1.06 |
| YTVOS | 507 | 4.51 | 13.0% | 1.03 |
| MOSE | 311 | 8.68* | 41.5% | 1.06 |
| SA-V | 155 | 17.24 | 25.5% | 1.09 |
| LVOS | 140 | 78.36 | 7.8% | 1.47 |
| **SeCVOS (ours)** | 160 | 29.36 | 30.2% | **4.26** |
## License
Our annotations are licensed under a [CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/). They are available strictly for non-commercial research.
We uphold the rights of individuals and copyright holders. If you are featured in any of our video annotations or hold copyright to a video and wish to have its annotation removed from our dataset, please reach out to us. Send an email to zhangzhixiong@pjlab.org.cn with the subject line beginning with SeCVOS, or raise an issue with the same title format. We commit to reviewing your request promptly and taking suitable action.
---
## Citation
If you find this project useful in your research, please consider citing:
```BibTeX
@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}
}
```
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