This is the official PyTorch implementation of:

VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer

CVPR 2026

• Language-Free: VisualAD removes the text encoder entirely and learns anomaly/normal prototypes purely in the visual feature space.
• Two Learnable Tokens: An anomaly token and a normal token are inserted into a frozen ViT, interacting with patch tokens through multi-layer self-attention to encode normality and abnormality.
• SCA & SAF Modules: Spatial-Aware Cross-Attention (SCA) injects fine-grained spatial evidence into the tokens; Self-Alignment Function (SAF) recalibrates patch features before anomaly scoring.
• 13 Benchmarks: State-of-the-art performance across 6 industrial and 7 medical zero-shot anomaly detection benchmarks.
• Backbone Agnostic: Adapts seamlessly to CLIP (ViT-L/14@336px) and DINOv2 (ViT-L/14).

All backbones use ViT-L/14@336px (CLIP) or ViT-L/14 (DINOv2). Full results including medical benchmarks (OCT17, BrainMRI, Brain_AD, HIS, CVC-ClinicDB, Endo, Kvasir) are available in our paper.

pip install -r requirements.txt

Main dependencies: PyTorch >= 2.0, torchvision, timm, scikit-learn, scipy, tqdm.

VisualAD is evaluated on 13 zero-shot anomaly detection benchmarks across industrial inspection and medical imaging. The dataset packages used by this project are available below:

We adopt the same dataset structure and JSON format as AnomalyCLIP. After downloading a dataset, place it under your local dataset directory and make sure meta.json exists in the dataset root. We also provide scripts to generate the required JSON metadata files:

python generate_dataset_json/mvtec.py
python generate_dataset_json/visa.py

We provide pre-trained checkpoints with the CLIP (ViT-L/14@336px) backbone:

Run the full cross-dataset training and evaluation pipeline (MVTec-AD <-> VisA) with a single command:

bash scripts/CLIP.sh

Please modify the dataset paths in scripts/CLIP.sh before running.

If you find this work useful, please consider citing:

@InProceedings{Hou_2026_CVPR,
  author    = {Hou, Yanning and Li, Peiyuan and Liu, Zirui and Wang, Yitong and Ruan, Yanran and Qiu, Jianfeng and Xu, Ke},
  title     = {VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2026},
  pages     = {21346-21356}
}

This project builds upon CLIP and DINOv2. We thank the authors of AnomalyCLIP and AdaCLIP for their open-source implementations.

If you have any questions, feel free to reach out:

• Email: yanning_hou@nudt.edu.cn
• WeChat: HYNing777