24 citations · 29 across the 16 of their papers we have counts for
16 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…