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--- |
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base_model: |
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- OpenGVLab/InternVL2.5-4B |
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- facebook/sam2.1-hiera-large |
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license: apache-2.0 |
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pipeline_tag: image-segmentation |
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tags: |
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- SeC |
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library_name: transformers |
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--- |
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# SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction |
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[\[📂 GitHub\]](https://github.com/OpenIXCLab/SeC) |
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[\[📦 Benchmark\]](https://huggingface.co/datasets/OpenIXCLab/SeCVOS) |
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[\[🌐 Homepage\]](https://rookiexiong7.github.io/projects/SeC/) |
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[\[📄 Paper\]](https://arxiv.org/abs/2507.15852) |
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## Highlights |
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- 🔥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. |
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- 🔥SeC dynamically balances **semantic reasoning** with **feature matching**, adaptively adjusting computational efforts based on **scene complexity** for optimal segmentation performance. |
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- 🔥We propose the **Semantic Complex Scenarios Video Object Segmentation (SeCVOS)** benchmark, designed to evaluate segmentation in challenging scenarios. |
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## SeC Performance |
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| Model | SA-V val | SA-V test | LVOS v2 val | MOSE val | DAVIS 2017 val | YTVOS 2019 val | SeCVOS | |
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| :------ | :------: | :------: | :------: | :------: | :------: | :------: | :------: | |
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| SAM 2.1 | 78.6 | 79.6 | 84.1 | 74.5 | 90.6 | 88.7 | 58.2 | |
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| SAMURAI | 79.8 | 80.0 | 84.2 | 72.6 | 89.9 | 88.3 | 62.2 | |
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| SAM2.1Long | 81.1 | 81.2 | 85.9 | 75.2 | 91.4 | 88.7 | 62.3 | |
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| **SeC (Ours)** | **82.7** | **81.7** | **86.5** | **75.3** | **91.3** | **88.6** | **70.0** | |
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--- |
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## Usage |
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You can load the SeC model and processor using the `transformers` library with `trust_remote_code=True`. For comprehensive video object segmentation and detailed usage instructions, please refer to the project's [GitHub repository](https://github.com/OpenIXCLab/SeC), particularly `demo.ipynb` for single video inference and `INFERENCE.md` for full inference and evaluation. |
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```python |
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import torch |
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from transformers import AutoModel, AutoProcessor |
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from PIL import Image |
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# Load model and processor |
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model_name = "OpenIXCLab/SeC-4B" |
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# Ensure your environment has the necessary PyTorch and transformers versions as specified in the GitHub repo. |
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model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() |
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processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) |
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# Example: Assuming you have an image (e.g., a frame from a video) and a text query |
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# For full video processing, refer to the project's GitHub repository. |
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# Placeholder for an actual image path |
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# image = Image.open("path/to/your/image.jpg").convert("RGB") |
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# text_query = "segment the main object" |
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# # Prepare inputs |
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# inputs = processor(images=image, text=text_query, return_tensors="pt").to(model.device) |
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# # Perform inference |
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# with torch.no_grad(): |
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# outputs = model(**inputs) |
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# The output format will vary depending on the model's implementation. |
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# Typically, for segmentation tasks, outputs might include logits or predicted masks. |
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# You will need to process these outputs further to visualize the segmentation. |
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print("Model loaded successfully. For actual inference with video data, please refer to the project's GitHub repository and demo.ipynb.") |
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``` |
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## Citation |
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If you find this project useful in your research, please consider citing: |
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```BibTeX |
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@article{zhang2025sec, |
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title = {SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction}, |
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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}, |
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journal = {arXiv preprint arXiv:2507.15852}, |
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year = {2025} |
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} |
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``` |
