| --- |
| 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} |
| } |
| ``` |
