  Unsupervised visual anomaly detection conveys practical significance in many
scenarios and is a challenging task due to the unbounded definition of
anomalies. Moreover, most previous methods are application-specific, and
establishing a unified model for anomalies across application scenarios remains
unsolved. This paper proposes a novel hybrid framework termed Siamese
Transition Masked Autoencoders(ST-MAE) to handle various visual anomaly
detection tasks uniformly via deep feature transition. Concretely, the proposed
method first extracts hierarchical semantics features from a pre-trained deep
convolutional neural network and then develops a feature decoupling strategy to
split the deep features into two disjoint feature patch subsets. Leveraging the
decoupled features, the ST-MAE is developed with the Siamese encoders that
operate on each subset of feature patches and perform the latent
representations transition of two subsets, along with a lightweight decoder
that reconstructs the original feature from the transitioned latent
representation. Finally, the anomalous attributes can be detected using the
semantic deep feature residual. Our deep feature transition scheme yields a
nontrivial and semantic self-supervisory task to extract prototypical normal
patterns, which allows for learning uniform models that generalize well for
different visual anomaly detection tasks. The extensive experiments conducted
demonstrate that the proposed ST-MAE method can advance state-of-the-art
performance on multiple benchmarks across application scenarios with a superior
inference efficiency, which exhibits great potential to be the uniform model
for unsupervised visual anomaly detection.
