  Image anomaly detection problems aim to determine whether an image is
abnormal, and to detect anomalous areas. These methods are actively used in
various fields such as manufacturing, medical care, and intelligent
information. Encoder-decoder structures have been widely used in the field of
anomaly detection because they can easily learn normal patterns in an
unsupervised learning environment and calculate a score to identify
abnormalities through a reconstruction error indicating the difference between
input and reconstructed images. Therefore, current image anomaly detection
methods have commonly used convolutional encoder-decoders to extract normal
information through the local features of images. However, they are limited in
that only local features of the image can be utilized when constructing a
normal representation owing to the characteristics of convolution operations
using a filter of fixed size. Therefore, we propose a vision transformer-based
encoder-decoder model, named AnoViT, designed to reflect normal information by
additionally learning the global relationship between image patches, which is
capable of both image anomaly detection and localization. The proposed approach
constructs a feature map that maintains the existing location information of
individual patches by using the embeddings of all patches passed through
multiple self-attention layers. The proposed AnoViT model performed better than
the convolution-based model on three benchmark datasets. In MVTecAD, which is a
representative benchmark dataset for anomaly localization, it showed improved
results on 10 out of 15 classes compared with the baseline. Furthermore, the
proposed method showed good performance regardless of the class and type of the
anomalous area when localization results were evaluated qualitatively.
