128 citations · 178 across the 22 of their papers we have counts for
22 papers
Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection
Yihan Sun, Yuqi Cheng, Junjie Zu +5
Industrial 3D anomaly detection performance is fundamentally constrained by the scarcity and long-tailed distribution of abnormal samples. To address this challenge, we propose Syn…
Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection
Yuxin Jiang, Yunkang Cao, Weiming Shen
Few-shot anomaly detection (FSAD) denotes the identification of anomalies within a target category with a limited number of normal samples. Existing FSAD methods largely rely on pr…
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…
Leveraging Learning Bias for Noisy Anomaly Detection
Yuxin Zhang, Yunkang Cao, Yuqi Cheng +2
This paper addresses the challenge of fully unsupervised image anomaly detection (FUIAD), where training data may contain unlabeled anomalies. Conventional methods assume anomaly-f…
Multi-View Reconstruction with Global Context for 3D Anomaly Detection
Yihan Sun, Yuqi Cheng, Yunkang Cao +2
3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detectio…
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…
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
Yuqi Cheng, Yihan Sun, Hui Zhang +2
In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disp…
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…
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
Yunkang Cao, Yuqi Cheng, Xiaohao Xu +6
The practical deployment of Visual Anomaly Detection (VAD) systems is hindered by their sensitivity to real-world imaging variations, particularly the complex interplay between vie…
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…
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…
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework
Yuqi Cheng, Yunkang Cao, Guoyang Xie +2
Detecting anomalies within point clouds is crucial for various industrial applications, but traditional unsupervised methods face challenges due to data acquisition costs, early-st…
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
Yunkang Cao, Jiangning Zhang, Luca Frittoli +3
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveragin…
RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection
Yuqi Cheng, Yunkang Cao, Rui Chen +1
Robustness against noisy imaging is crucial for practical image anomaly detection systems. This study introduces a Robust Anomaly Detection (RAD) dataset with free views, uneven il…
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…
LogiCode: an LLM-Driven Framework for Logical Anomaly Detection
Yiheng Zhang, Yunkang Cao, Xiaohao Xu +1
This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond traditional focu…
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection
Yiheng Zhang, Yunkang Cao, Tianhang Zhang +1
This study targets Multi-Lighting Image Anomaly Detection (MLIAD), where multiple lighting conditions are utilized to enhance imaging quality and anomaly detection performance. Whi…
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning
Xiaohao Xu, Yunkang Cao, Huaxin Zhang +2
Anomaly detection is vital in various industrial scenarios, including the identification of unusual patterns in production lines and the detection of manufacturing defects for qual…
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Yunkang Cao, Xiaohao Xu, Jiangning Zhang +4
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspect…
Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense Prediction
Zhaoge Liu, Xiaohao Xu, Yunkang Cao +1
Knowledge distillation is the process of transferring knowledge from a more powerful large model (teacher) to a simpler counterpart (student). Numerous current approaches involve t…
Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead
Yunkang Cao, Xiaohao Xu, Chen Sun +2
Anomaly detection is a crucial task across different domains and data types. However, existing anomaly detection models are often designed for specific domains and modalities. This…