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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 1–10 of 39 papers

TitleStatusHype
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
Pyramid-based Mamba Multi-class Unsupervised Anomaly DetectionCode0
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly DetectionCode1
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly DetectionCode3
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly DetectionCode2
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection—0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection—0
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift—0
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly DetectionCode0
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