3 papers
cs.CV2026
Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
Wenbing Zhu, Chengjie Wang, Bin-Bin Gao +12
Industrial Anomaly Detection (IAD) is a cornerstone for ensuring operational safety, maintaining product quality, and optimizing manufacturing efficiency. However, the advancement…
cs.CV2025
Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection
Ziqing Zhou, Yurui Pan, Lidong Wang +4
Prototype-based reconstruction methods for unsupervised anomaly detection utilize a limited set of learnable prototypes which only aggregates insufficient normal information, resul…
cs.CV2025
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
Wenbing Zhu, Lidong Wang, Ziqing Zhou +14
The increasing complexity of industrial anomaly detection (IAD) has positioned multimodal detection methods as a focal area of machine vision research. However, dedicated multimoda…