  In this paper, we introduce the novel state-of-the-art Dual-attention
Transformer and Discriminative Flow (DADF) framework for visual anomaly
detection. Based on only normal knowledge, visual anomaly detection has wide
applications in industrial scenarios and has attracted significant attention.
However, most existing methods fail to meet the requirements. In contrast, the
proposed DTDF presents a new paradigm: it firstly leverages a pre-trained
network to acquire multi-scale prior embeddings, followed by the development of
a vision Transformer with dual attention mechanisms, namely self-attention and
memorial-attention, to achieve two-level reconstruction for prior embeddings
with the sequential and normality association. Additionally, we propose using
normalizing flow to establish discriminative likelihood for the joint
distribution of prior and reconstructions at each scale. The DADF achieves
98.3/98.4 of image/pixel AUROC on Mvtec AD; 83.7 of image AUROC and 67.4 of
pixel sPRO on Mvtec LOCO AD benchmarks, demonstrating the effectiveness of our
proposed approach.
