Multiresolution Feature Guidance Based Transformer for Anomaly Detection
This addresses the problem of detecting and localizing anomalies in images for applications like industrial inspection, though it is incremental as it builds on existing Transformer and pre-trained network methods.
The paper tackled unsupervised anomaly detection and localization by proposing GTrans, a multiresolution feature guidance method based on Transformer, which achieved state-of-the-art performance with image-level AUROC of 99.0% and pixel-level AUROC of 97.9% on the MVTec AD dataset.
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.