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