DiffusionInst: Diffusion Model for Instance Segmentation

[More updates on 2023.1.1] If you have any questions, please move to https://github.com/chenhaoxing/DiffusionInst.

DiffusionInst is the first work of diffusion model for instance segmentation. We hope our work could serve as a simple yet effective baseline, which could inspire designing more efficient diffusion frameworks for challenging discriminative tasks.

DiffusionInst: Diffusion Model for Instance Segmentation
Zhangxuan Gu, Haoxing Chen, Zhuoer Xu, Jun Lan, Changhua Meng, Weiqiang Wang arXiv 2212.02773

Todo list:

• Release source code.
• Adding directly filter denoising.

Getting Started

The installation instruction and usage are in Getting Started with DiffusionInst.

Model Performance

Method	Mask AP (1 step)	Mask AP (4 step)
COCO-Res50	35.1	35.5
COCO-Res101	36.3	36.5
COCO-Swin-B	44.0	44.2
LVIS-Res50	22.3	-
LVIS-Res101	24.6	-
LVIS-Swin-B	34.8	-

Citing DiffusionInst

If you use DiffusionInst in your research or wish to refer to the baseline results published here, please use the following BibTeX entry.

@article{DiffusionInst,
      title={DiffusionInst: Diffusion Model for Instance Segmentation},
      author={Gu, Zhangxuan and Chen, Haoxing and Xu, Zhuoer and Lan, Jun and Meng, Changhua and Wang, Weiqiang},
      journal={arXiv preprint arXiv:2212.02773},
      year={2022}
}

Acknowledgement

Many thanks to the nice work of DiffusionDet @ShoufaChen. Our codes and configs follow DiffusionDet.

Contacts

Please feel free to contact us if you have any problems.

Email: haoxingchen@smail.nju.edu.cn or guzhangxuan.gzx@antgroup.com