[NeurIPS 24] MetaUAS

Official PyTorch Implementation of MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning, NeurIPS 2024.

Updates

[2026-02-05]: Added 1-shot MetaUAS results on Real-IAD and Real-IAD-Variety datasets.

| Datasets | Methods | I-ROC | I-PR | I-F1 | P-ROC | P-PR | P-F1 | P-PRO | | :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | Real-IAD | MetaUAS (1-shot) | 80.0 ± 0.4 | 77.9 ± 0.4 | 72.4 ± 0.4 | 95.6 ± 0.2 | 36.6 ± 1.1 | 39.7 ± 1.0 | 83.5 ± 0.7 | | Real-IAD-Variety | MetaUAS (1-shot) | 81.9 ± 0.1 | 96.3 ± 0.1 | 94.1 ± 0.0 | 92.0 ± 0.1 | 48.2 ± 0.4 | 48.3 ± 0.3 | 76.5 ± 0.1 |

Introduction

MetaUAS unifies anomaly segmentation into change segmentation and provides a pure visual foundation model, which requires only one normal image prompt and no additional training, and effectively and efficiently segments any visual anomalies. MetaUAS significantly outperforms most zero-shot, few-shot, and even full-shot anomaly segmentation methods.

MetaUAS Framework

overview

Main Results

main-results

main-com-eff

main-diffprompts

Demo

You can use our Online Demo to test your custom data for a quick start. Note that the online demo is currently based on CPU. You could also deploy the demo application to your local CPU/GPU server using the following command:

pip install -r requirements.txt
python app.py

Evaluation on MVTec/VisA/GoodsAD

bash test.sh

ToDo List

[x] release pre-trained MetaUAS models
[x] deploy online MetaUAS Demo
[x] open testing code
[ ] open training code

Citing

If you find this code useful in your research, please consider citing us:

@inproceedings{gao2024metauas,
  title  = {MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning},
  author = {Gao, Bin-Bin},
  booktitle = {Advances in Neural Information Processing Systems},
  pages = {39812--39836},
  year = {2024}
}

MetaUAS

Install / Use

README