  Humans recognize anomalies through two aspects: larger patch-wise
representation discrepancies and weaker patch-to-normal-patch correlations.
However, the previous AD methods didn't sufficiently combine the two
complementary aspects to design AD models. To this end, we find that
Transformer can ideally satisfy the two aspects as its great power in the
unified modeling of patch-wise representations and patch-to-patch correlations.
In this paper, we propose a novel AD framework: FOcus-the-Discrepancy (FOD),
which can simultaneously spot the patch-wise, intra- and inter-discrepancies of
anomalies. The major characteristic of our method is that we renovate the
self-attention maps in transformers to Intra-Inter-Correlation (I2Correlation).
The I2Correlation contains a two-branch structure to first explicitly establish
intra- and inter-image correlations, and then fuses the features of two-branch
to spotlight the abnormal patterns. To learn the intra- and inter-correlations
adaptively, we propose the RBF-kernel-based target-correlations as learning
targets for self-supervised learning. Besides, we introduce an entropy
constraint strategy to solve the mode collapse issue in optimization and
further amplify the normal-abnormal distinguishability. Extensive experiments
on three unsupervised real-world AD benchmarks show the superior performance of
our approach. Code will be available at https://github.com/xcyao00/FOD.
