6 papers
Template-Based Feature Aggregation Network for Industrial Anomaly Detection
Wei Luo, Haiming Yao, Wenyong Yu
Industrial anomaly detection plays a crucial role in ensuring product quality control. Therefore, proposing an effective anomaly detection model is of great significance. While exi…
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Wei Luo, Yunkang Cao, Haiming Yao +5
Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, v…
INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning
Wei Luo, Haiming Yao, Yunkang Cao +4
Anomaly detection (AD) is essential for industrial inspection and medical diagnosis, yet existing methods typically rely on ``comparing'' test images to normal references from a tr…
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization
Wei Luo, Haiming Yao, Wenyong Yu +1
Unsupervised visual anomaly detection is crucial for enhancing industrial production quality and efficiency. Among unsupervised methods, reconstruction approaches are popular due t…
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection
Haiming Yao, Yunkang Cao, Wei Luo +3
Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deploym…
Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection
Haiming Yao, Wei Luo, Yunkang Cao +3
Texture surface anomaly detection finds widespread applications in industrial settings. However, existing methods often necessitate gathering numerous samples for model training. M…