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---
license: apache-2.0
pipeline_tag: mask-generation
base_model:
- OpenGVLab/InternVL2.5-4B
- facebook/sam2.1-hiera-large
tags:
- SeC
---
# SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction
[\[📂 GitHub\]](https://github.com/OpenIXCLab/SeC)
[\[📦 Benchmark\]](https://huggingface.co/datasets/OpenIXCLab/SeCVOS)
[\[🌐 Homepage\]](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.
## 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** |
---
## 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}
}
``` |