Detecting anomalies in industrial settings is challenging due to the scarcity of labeled anomalous data. Generative models can mitigate this issue by synthesizing realistic defect samples, but existing approaches often fail to model the crucial interplay between defects and their background. This oversight leads to unrealistic anomalies, especially in scenarios where contextual consistency is essential (i.e., logical anomaly). To address this, we propose a novel background-aware defect generation framework, where the background influences defect denoising without affecting the background itself by ensuring realistic synthesis while preserving structural integrity. Our method leverages a disentanglement loss to separate the background' s denoising process from the defect, enabling controlled defect synthesis through DDIM Inversion. We theoretically demonstrate that our approach maintains background fidelity while generating contextually accurate defects. Extensive experiments on MVTec AD and MVTec Loco benchmarks validate our mehtod's superiority over existing techniques in both defect generation quality and anomaly detection performance.
The Course of Precision Engineering, School of Science and Engineering, Chuo University（中央大学理工学部精密工学科）；Chuo University（中央大学）；Prima Meat Packers, Ltd.（プリマハム株式会社（普瑞玛肉制品包装有限公司））
This paper proposes an anomaly detection method for the prevention of industrial accidents using machine learning technology.
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Jiangxi Normal University（江西师范大学）；Southern University of Science and Technology（南方科技大学）；Swansea University（斯旺西大学）；Zhejiang University（浙江大学）；Anhui University（安徽大学）；Northwestern Polytechnical University（西北工业大学）
Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data
Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University（江南大学江苏省模式识别与计算智能工程实验室）；College of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）
Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；University of Surrey（萨里大学）；Tencent（腾讯）；Bielefeld University（比勒费尔德大学）
School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computing and Information Technology, Great Bay University（海湾大学计算机与信息学院）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））
Identifying defects in the images of industrial products has been an important task to enhance quality control and reduce maintenance costs. In recent studies, industrial anomaly detection models were developed using pre-trained networks to learn nominal representations. To employ the relative positional information of each pixel, we present \textit{\textbf{N-pad}}, a novel method for anomaly detection and segmentation in a one-class learning setting that includes the neighborhood of the target pixel for model training and evaluation. Within the model architecture, pixel-wise nominal distribut…
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some methods do not meet the speed requirements of real-time inference, which is crucial for real-world applications. To address this issue, we propose a new method called Fast Adaptive Patch Memory (FAPM) for real-time industrial anomaly detection. FAPM utilizes patch-wise and layer-wise memory banks that store the embedding features of images at the patch and layer level, respectively, which eliminates unnecessary repetitive …
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University（清华大学精密仪器测量技术及仪器国家重点实验室）
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CASI Vision Technology CO., LTD.（CASI视觉技术有限公司）
Defect detection in the manufacturing industry is of utmost importance for product quality inspection. Recently, optical defect detection has been investigated as an anomaly detection using different deep learning methods. However, the recent works do not explore the use of point pattern features, such as SIFT for anomaly detection using the recently developed set-based methods. In this paper, we present an evaluation of different point pattern feature detectors and descriptors for defect detection application. The evaluation is performed within the random finite set framework. Handcrafted poi…
Gauss Labs Inc.（高斯实验室公司）；Seoul National University（首尔国立大学）
Because anomalous samples cannot be used for training, many anomaly detection and localization methods use pre-trained networks and non-parametric modeling to estimate encoded feature distribution. However, these methods neglect the impact of position and neighborhood information on the distribution of normal features. To overcome this, we propose a new algorithm, \textbf{PNI}, which estimates the normal distribution using conditional probability given neighborhood features, modeled with a multi-layer perceptron network. Moreover, position information is utilized by creating a histogram of rep…
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
Academy for Engineering & Technology, Fudan University（复旦大学工程技术学院）；Shanghai Engineering Research Center of AI & Robotics（上海市人工智能与机器人工程研究中心）；Engineering Research Center of AI & Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）
Multimodal Industrial Anomaly Detection via Geometric Prior
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（教育部计算功率网络与信息安全重点实验室，山东计算机科学中心（济南国家超算中心），齐鲁工业大学（山东省科学院））；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science（山东省计算功率互联网与服务计算重点实验室，山东省计算机科学基础研究中心）
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets. These approaches work by learning to model normal features without seeing abnormal samples during training and then discriminating anomalies at test time based on the reconstructive errors. However, these models have limitations in reconstructing the abnormal samples due to their indiscriminate conveyance of features. Moreover, these approaches are not explicitly optimized for distinguishable anomalies. To address these problems, we propose a two-stream de…
Leibniz University Hannover（汉诺威莱布尼茨大学）；Linköping University（林雪平大学）
Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available. This work discovers previously unknown problems of student-teacher approaches for AD and proposes a solution, where two neural networks are trained to produce the same output for the defect-free training examples. The core assumption of student-teacher networks is that the distance between the outputs of both networks is larger for anomalies since they are absent in training. However, previous methods suffer from the similarity of…
IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools
State Key Lab of Processors, Institute of Computing Technology, CAS（处理器国家重点实验室，计算技术研究所，中国科学院）；Santa Clara University（圣克拉拉大学）；LongCat Team（LongCat团队）；Independent Researcher（独立研究者）；New York University（纽约大学）；Sun Yat-sen University（孙中山大学）；Nanyang Technological University（南洋理工大学）；Stanford University（斯坦福大学）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）
BridgeNet: A Unified Multimodal Framework for Bridging 2D and 3D Industrial Anomaly Detection
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；State Key Lab of IOTSC, Department of CIS, University of Macau（物联网科学国家重点实验室，澳门大学）
AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Hubei Key Laboratory of Smart Internet Technology, Huazhong University of Science and Technology（华中科技大学智能互联网技术重点实验室）；Artificial Intelligence Research Institute, Wuhan JingCe Electronic Group Co., LTD（武汉景创电子集团有限公司人工智能研究所）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）
