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Multi-class Anomaly Detection

Multi-class Anomaly Detection is a task that identifies anomalies by jointly learning and detecting outliers across multiple classes, in contrast to traditional Anomaly Detection, which typically focuses on identifying anomalies within a single class.

Papers

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Showing 11–20 of 39 papers

TitleStatusHype
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly DetectionCode1
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly DetectionCode1
UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly DetectionCode1
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning—0
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment—0
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization—0
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection—0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift—0
AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model—0
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