  Anomaly detection in computer vision is the task of identifying images which
deviate from a set of normal images. A common approach is to train deep
convolutional autoencoders to inpaint covered parts of an image and compare the
output with the original image. By training on anomaly-free samples only, the
model is assumed to not being able to reconstruct anomalous regions properly.
For anomaly detection by inpainting we suggest it to be beneficial to
incorporate information from potentially distant regions. In particular we pose
anomaly detection as a patch-inpainting problem and propose to solve it with a
purely self-attention based approach discarding convolutions. The proposed
Inpainting Transformer (InTra) is trained to inpaint covered patches in a large
sequence of image patches, thereby integrating information across large regions
of the input image. When learning from scratch, InTra achieves better than
state-of-the-art results on the MVTec AD [1] dataset for detection and
localization.
