English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Unsupervised visual anomaly detection conveys practical significance in manyscenarios and is a challenging task due to the unbounded definition ofanomalies. Moreover, most previous methods are application-specific, andestablishing a unified model for anomalies across application scenarios remainsunsolved. This paper proposes a novel hybrid framework termed SiameseTransition Masked Autoencoders(ST-MAE) to handle various visual anomalydetection tasks uniformly via deep feature transition. Concretely, the proposedmethod first extracts hierarchical semantics features from a pre-trained deepconvolutional neural network and then develops a feature decoupling strategy tosplit the deep features into two disjoint feature patch subsets. Leveraging thedecoupled features, the ST-MAE is developed with the Siamese encoders thatoperate on each subset of feature patches and perform the latentrepresentations transition of two subsets, along with a lightweight decoderthat reconstructs the original feature from the transitioned latentrepresentation. Finally, the anomalous attributes can be detected using thesemantic deep feature residual. Our deep feature transition scheme yields anontrivial and semantic self-supervisory task to extract prototypical normalpatterns, which allows for learning uniform models that generalize well fordifferent visual anomaly detection tasks. The extensive experiments conducteddemonstrate that the proposed ST-MAE method can advance state-of-the-artperformance on multiple benchmarks across application scenarios with a superiorinference efficiency, which exhibits great potential to be the uniform modelfor unsupervised visual anomaly detection.

Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector