Image anomaly detection consists in detecting images or image portions that
are visually different from the majority of the samples in a dataset. The task
is of practical importance for various real-life applications like biomedical
image analysis, visual inspection in industrial production, banking, traffic
management, etc. Most of the current deep learning approaches rely on image
reconstruction: the input image is projected in some latent space and then
reconstructed, assuming that the network (mostly trained on normal data) will
not be able to reconstruct the anomalous portions. However, this assumption
does not always hold. We thus propose a new model based on the Vision
Transformer architecture with patch masking: the input image is split in
several patches, and each patch is reconstructed only from the surrounding
data, thus ignoring the potentially anomalous information contained in the
patch itself. We then show that multi-resolution patches and their collective
embeddings provide a large improvement in the model's performance compared to
the exclusive use of the traditional square patches. The proposed model has
been tested on popular anomaly detection datasets such as MVTec and head CT and
achieved good results when compared to other state-of-the-art approaches.

基于 Vision Transformer 架构和 Patch 掩蔽技术的图像异常检测方法，将输入图像分成多个分辨率的 Patch，在忽略异常信息的同时对周围数据进行重建，相较传统方法性能更好，MVTec 和 head CT 等数据集上实现了良好的结果。