README.md

---
base_model:
- OpenGVLab/InternVL2.5-4B
- facebook/sam2.1-hiera-large
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- SeC
library_name: transformers
---

# 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** |

---

## Usage

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.

```python
import torch
from transformers import AutoModel, AutoProcessor
from PIL import Image

# Load model and processor
model_name = "OpenIXCLab/SeC-4B"
# Ensure your environment has the necessary PyTorch and transformers versions as specified in the GitHub repo.
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)

# Example: Assuming you have an image (e.g., a frame from a video) and a text query
# For full video processing, refer to the project's GitHub repository.
# Placeholder for an actual image path
# image = Image.open("path/to/your/image.jpg").convert("RGB")
# text_query = "segment the main object"

# # Prepare inputs
# inputs = processor(images=image, text=text_query, return_tensors="pt").to(model.device)

# # Perform inference
# with torch.no_grad():
#     outputs = model(**inputs)

# The output format will vary depending on the model's implementation.
# Typically, for segmentation tasks, outputs might include logits or predicted masks.
# You will need to process these outputs further to visualize the segmentation.
print("Model loaded successfully. For actual inference with video data, please refer to the project's GitHub repository and demo.ipynb.")
```

## 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}
}
```