Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector

by   Haiming Yao, et al.

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.


page 1

page 3

page 4

page 6

page 8

page 9

page 11

page 13


Anomaly Detection with Adversarially Learned Perturbations of Latent Space

Anomaly detection is to identify samples that do not conform to the dist...

Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection

Unsupervised anomaly detection from high dimensional data like mobility ...

FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Unsupervised anomaly detection and localization is crucial to the practi...

SelFormaly: Towards Task-Agnostic Unified Anomaly Detection

The core idea of visual anomaly detection is to learn the normality from...

DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Automatic detecting anomalous regions in images of objects or textures w...

A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection

Anomalies can be defined as any non-random structure which deviates from...

Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer

Industrial vision anomaly detection plays a critical role in the advance...

Please sign up or login with your details

Forgot password? Click here to reset