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README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+---
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+# 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.xxxxx)
+
+## 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 [email protected] 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.