24 citations · 52 across the 23 of their papers we have counts for
23 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…
A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detection
Wei Luo, Haiming Yao, Zhenfeng Qiang +2
Unsupervised anomaly detection is vital in industrial fields, with reconstruction-based methods favored for their simplicity and effectiveness. However, reconstruction methods ofte…
URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection
Wei Luo, Peng Xing, Yunkang Cao +3
Unsupervised anomaly detection plays a pivotal role in industrial defect inspection and medical image analysis, with most methods relying on the reconstruction framework. However,…
Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation
Yuxin Jiang, Wei Luo, Hui Zhang +4
We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textu…
CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
Qiyu Chen, Zhen Qu, Wei Luo +7
Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the mode…
A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects
Yuqi Cheng, Yunkang Cao, Haiming Yao +4
Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensi…
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…
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
Qiyu Chen, Huiyuan Luo, Haiming Yao +4
Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment cos…
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…
Self-Calibrated Dual Contrasting for Annotation-Efficient Bacteria Raman Spectroscopy Clustering and Classification
Haiming Yao, Wei Luo, Tao Zhou +2
Raman scattering is based on molecular vibration spectroscopy and provides a powerful technology for pathogenic bacteria diagnosis using the unique molecular fingerprint informatio…
VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive Modeling
Yunkang Cao, Haiming Yao, Wei Luo +1
This paper addresses a practical task: High-Resolution Image Anomaly Detection (HRIAD). In comparison to conventional image anomaly detection for low-resolution images, HRIAD impos…
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…
Adversarial Contrastive Domain-Generative Learning for Bacteria Raman Spectrum Joint Denoising and Cross-Domain Identification
Haiming Yao, Wei Luo, Xue Wang
Raman spectroscopy, as a label-free detection technology, has been widely utilized in the clinical diagnosis of pathogenic bacteria. However, Raman signals are naturally weak and s…
DiffRaman: A Conditional Latent Denoising Diffusion Probabilistic Model for Bacterial Raman Spectroscopy Identification Under Limited Data Conditions
Haiming Yao, Wei Luo, Ang Gao +2
Raman spectroscopy has attracted significant attention in various biochemical detection fields, especially in the rapid identification of pathogenic bacteria. The integration of th…
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…
A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing
Haiming Yao, Wenyong Yu, Xue Wang
Recent advances in the industrial inspection of textured surfaces-in the form of visual inspection-have made such inspections possible for efficient, flexible manufacturing systems…