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ACM MM 2025: From Language to Instance: Generative Visual Prompting for Zero-shot Camouflaged Object Detection

Lip 최신버젼 다운로드

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# :fire: [ACM MM 2025] From Language to Instance: Generative Visual Prompting for Zero-shot Camouflaged Object Detection

Code release of paper:

[**From Language to Instance: Generative Visual Prompting for Zero-shot Camouflaged Object Detection**](https://dl.acm.org/doi/10.1145/3746027.3755212)

## :rocket: News
* **[2025.10.29]** The code of Lip is released!
* **[2025.07.05]** Lip is accepted to ACM MM 2025!

</p>
<p align="center">
  <img src="figall.png" width="100%" />
</p>

## Quick Start

### Download Dataset
1. Download the datasets from the follow links:
   
**Camouflaged Object Detection Dataset**
- **[CHAMELEON](https://www.polsl.pl/rau6/datasets/)**
- **[CAMO](https://drive.google.com/open?id=1h-OqZdwkuPhBvGcVAwmh0f1NGqlH_4B6)**
- **[COD10K](https://github.com/DengPingFan/SINet/)**
2. Put it in data/TestDataset
1. My code was implemented with Python 3.10 and PyTorch 2.1.0. We recommend creating environment and installing all the dependencies, as follows:
```bash
# create virtual environment
conda create --name Lip python=3.10
conda activate Lip
# prepare codebase
git clone https://github.com/skywalker0523/Lip.git
cd Lip
# prepare environment
pip install -r requirement.txt
# prepare dinov2
git clone https://github.com/facebookresearch/dinov2.git
# prepare SAM
pip install git+https://github.com/facebookresearch/segment-anything.git
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
```
2. Our Lip is a training-free test-time adaptation approach, so you can play with it by running:
```bash
python main.py --config config/CHAMELEON.yaml  
```

## Citation

If you find our work useful in your research, please consider citing:

```
@inproceedings{zhang2025language,
  title={From Language to Instance: Generative Visual Prompting for Zero-shot Camouflaged Object Detection},
  author={Zhang, Zihou and Li, Hao and Yang, Zhengwei and Hu, Zechao and Li, Liang and Wang, Zheng},
  booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
  pages={382--391},
  year={2025}
}
```

## :cupid: Acknowledgements

- [GenSAM](https://github.com/jyLin8100/GenSAM)
- [ProMaC](https://github.com/lwpyh/ProMaC_code)
- [MMCPF](https://arxiv.org/abs/2311.11273)
- [IPSeg](https://arxiv.org/abs/2310.10912)

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