  Anomaly detection is represented as an unsupervised learning to identify
deviated images from normal images. In general, there are two main challenges
of anomaly detection tasks, i.e., the class imbalance and the unexpectedness of
anomalies. In this paper, we propose a multiresolution feature guidance method
based on Transformer named GTrans for unsupervised anomaly detection and
localization. In GTrans, an Anomaly Guided Network (AGN) pre-trained on
ImageNet is developed to provide surrogate labels for features and tokens.
Under the tacit knowledge guidance of the AGN, the anomaly detection network
named Trans utilizes Transformer to effectively establish a relationship
between features with multiresolution, enhancing the ability of the Trans in
fitting the normal data manifold. Due to the strong generalization ability of
AGN, GTrans locates anomalies by comparing the differences in spatial distance
and direction of multi-scale features extracted from the AGN and the Trans. Our
experiments demonstrate that the proposed GTrans achieves state-of-the-art
performance in both detection and localization on the MVTec AD dataset. GTrans
achieves image-level and pixel-level anomaly detection AUROC scores of 99.0%
and 97.9% on the MVTec AD dataset, respectively.
