  This work studies the recently proposed challenging and practical Multi-class
Unsupervised Anomaly Detection (MUAD) task, which only requires normal images
for training while simultaneously testing both normal/anomaly images for
multiple classes. Existing reconstruction-based methods typically adopt pyramid
networks as encoders/decoders to obtain multi-resolution features, accompanied
by elaborate sub-modules with heavier handcraft engineering designs for more
precise localization. In contrast, a plain Vision Transformer (ViT) with simple
architecture has been shown effective in multiple domains, which is simpler,
more effective, and elegant. Following this spirit, this paper explores plain
ViT architecture for MUAD. Specifically, we abstract a Meta-AD concept by
inducing current reconstruction-based methods. Then, we instantiate a novel and
elegant plain ViT-based symmetric ViTAD structure, effectively designed step by
step from three macro and four micro perspectives. In addition, this paper
reveals several interesting findings for further exploration. Finally, we
propose a comprehensive and fair evaluation benchmark on eight metrics for the
MUAD task. Based on a naive training recipe, ViTAD achieves state-of-the-art
(SoTA) results and efficiency on the MVTec AD and VisA datasets without bells
and whistles, obtaining 85.4 mAD that surpasses SoTA UniAD by +3.0, and only
requiring 1.1 hours and 2.3G GPU memory to complete model training by a single
V100 GPU. Source code, models, and more results are available at
https://zhangzjn.github.io/projects/ViTAD.
