We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects. We bring the improvement upon previous arts by 3.1 AUCs when learning representations from scratch. By transfer learning on pretrained representations on ImageNet, we achieve a new state-of-theart 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localizing defective areas without annotations during training.
Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
SenseTime Research（商汤科技研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
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）
SimpleNet: A Simple Network for Image Anomaly Detection and Localization
University of Science Technology of China（中国科学技术大学）；Meka Technology Co Ltd（美卡科技有限公司）
我们提出了一个简单的和应用程序友好的网络（称为SimpleNet）检测和定位异常。SimpleNet由四个组件组成：（1）一个预训练的特征提取器，用于生成局部特征;（2）一个浅层特征适配器，用于将局部特征转移到目标域;（3）一个简单的异常特征生成器，通过向正常特征添加高斯噪声来伪造异常特征;以及（4）一个二进制异常鉴别器，用于区分异常特征和正常特征。在推理过程中，异常特征生成器将被丢弃。我们的方法基于三个直觉。首先，将预先训练的特征转换为面向目标的特征有助于避免领域偏见。其次，在特征空间中生成合成异常更有效，因为缺陷在图像空间中可能没有太多的共性。第三，一个简单的系统是非常有效和实用的。尽管简单，SimpleNet在定量和定性上优于以前的方法。在MVTec AD基准测试中，SimpleNet实现了99.6%的异常检测AUROC，与下一个表现最好的模型相比，误差减少了55.5%。此外，SimpleNet比现有方法更快，在3080 ti GPU上具有77 FPS的高帧速率。此外，SimpleNet在单类新奇检测任务的性能上有了显着的改进。代码：https://github.com/DonaldRR/SimpleNet.
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes
Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM））；Di Hub；Iran University of Science and Technology（伊朗科技大学）；Auckland University of Technology（奥克兰理工大学）
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心，自动化研究所，中国科学院）；School of Artifcial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Wuhan AI Research（武汉AI研究所）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University（广东省知识产权与大数据重点实验室，广东工业大学）
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）
SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection
Institute of Artificial Intelligence, University of Engineering and Technology, Vietnam National University（越南国立大学工程技术大学人工智能研究所）；Human-Machine Interaction Laboratory, University of Engineering and Technology, Vietnam National University（越南国立大学工程技术大学人机交互实验室）；Applied AI Lab, Phenikaa School of Computing, Phenikaa University（费尼卡大学计算学院应用人工智能实验室）
UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptation
Computer Aided Medical Procedures (CAMP)（计算机辅助医疗程序）；TU Munich, Germany（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所基础模型研究中心）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Objecteye Inc., Beijing, China（Objecteye公司）；Central South University, Hunan, China（中南大学）
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval
Jiangxi Normal University（江西师范大学）；Zhejiang University（浙江大学）；Northwestern Polytechnical University（西北工业大学）；National University of Defense Technology（国防科技大学）
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization
SenseTime Research（商汤科技研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加州大学洛杉矶分校）
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization
organization= School of Software Engineering,Xi'an Jiaotong University , addressline= 28 Xianning West Road , city= Xi'an , postcode= 710049 , state= Shaanxi , country= China
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography
Samsung Medical Center（三星医疗中心）；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Sungkyunkwan University（成均馆大学）
