9 citations · 31 across the 13 of their papers we have counts for
13 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…
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…
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…
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…
SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects
Peng Wang, Haiming Yao, Wenyong Yu
The unsupervised visual inspection of defects in industrial products poses a significant challenge due to substantial variations in product surfaces. Current unsupervised models st…
Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flow
Haiming Yao, Wei Luo, Wenyong Yu
In this paper, we introduce the novel state-of-the-art Dual-attention Transformer and Discriminative Flow (DADF) framework for visual anomaly detection. Based on only normal knowle…
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection
Haiming Yao, Wenyong Yu, Wei Luo +3
This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has becom…
Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer
Haiming Yao, Wenyong Yu
Industrial vision anomaly detection plays a critical role in the advanced intelligent manufacturing process, while some limitations still need to be addressed under such a context.…
Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection
Wei Luo, Haiming Yao, Wenyong Yu
Visual anomaly detection plays a significant role in the development of industrial automatic product quality inspection. As a result of the utmost imbalance in the amount of normal…
Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector
Haiming Yao, Xue Wang, Wenyong Yu
Unsupervised visual anomaly detection conveys practical significance in many scenarios and is a challenging task due to the unbounded definition of anomalies. Moreover, most previo…
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection
Wei Luo, Tongzhi Niu, Lixin Tang +2
In surface defect detection, due to the extreme imbalance in the number of positive and negative samples, positive-samples-based anomaly detection methods have received more and mo…