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Unsupervised anomaly detection and localization is a crucial task as it isimpossible to collect and label all possible anomalies. Many studies haveemphasized the importance of integrating local and global information toachieve accurate segmentation of anomalies. To this end, there has been agrowing interest in Transformer, which allows modeling long-range contentinteractions. However, global interactions through self attention are generallytoo expensive for most image scales. In this study, we introduce HaloAE, thefirst auto-encoder based on a local 2D version of Transformer with HaloNet.With HaloAE, we have created a hybrid model that combines convolution and local2D block-wise self-attention layers and jointly performs anomaly detection andsegmentation through a single model. We achieved competitive results on theMVTec dataset, suggesting that vision models incorporating Transformer couldbenefit from a local computation of the self-attention operation, and pave theway for other applications.

HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization