Dual-path Frequency Discriminators for Few-shot Anomaly Detection
Zhejiang University（浙江大学）；Polytechnic Institute, Zhejiang University（浙江大学理工学院）；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Tencent（腾讯）；China Tower (Hangzhou) Science and Technology Innovation Center（中国铁塔（杭州）科技创新中心）；School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）
Few-shot anomaly detection (FSAD) plays a crucial role in industrial manufacturing. However, existing FSAD methods encounter difficulties leveraging a limited number of normal samples, frequently failing to detect and locate inconspicuous anomalies in the spatial domain. We have further discovered that these subtle anomalies would be more noticeable in the frequency domain. In this paper, we propose a Dual-Path Frequency Discriminators (DFD) network from a frequency perspective to tackle these issues. The original spatial images are transformed into multi-frequency images, making them more conducive to the tailored discriminators in detecting anomalies. Additionally, the discriminators learn a joint representation with forms of pseudo-anomalies. Extensive experiments conducted on MVTec AD and VisA benchmarks demonstrate that our DFD surpasses current state-of-the-art methods. The code is available at \url{https://github.com/yuhbai/DFD}.
DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Microsoft Research Asia（微软亚洲研究院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
CLIP-Flow: A Universal Discriminator for AI-Generated Images Inspired by Anomaly Detection
Jilin University（吉林大学）；GIPSA-lab, Univ. Grenoble Alpes, CNRS, Grenoble INP（GIPSA实验室，格勒诺布尔阿尔卑斯大学，CNRS，格勒诺布尔INP）；Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）
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.
Discriminative-Generative Representation Learning for One-Class Anomaly Detection
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Institute of Robotics and Intelligent Manufacturing, Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）机器人与智能制造研究院）
AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation
School of Computer Science and Engineering（计算机科学与工程学院）；Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（大数据分析与处理广东省重点实验室）
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局信息通信研究院）；Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）
PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection
Harbin Engineering University（哈尔滨工程大学）；Key Laboratory of Advanced Marine Communication and Information Technology（先进海洋通信与信息技术重点实验室）；University of Toronto（多伦多大学）；Kanagawa University（神奈川大学）
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人机混合增强智能全国重点实验室与人工智能与机器人研究所）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Xiamen University（厦门大学）；Shenzhen University（深圳大学）
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）
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；King’s College London（伦敦国王学院）
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
University of Science and Technology of China（中国科学技术大学）；Northeastern University（东北大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts
School of Artificial Intelligence, Anhui University, Hefei, China（人工智能学院，安徽大学，合肥，中国）；Anhui Provincial Key Laboratory of Security Artificial Intelligence, Anhui University（安徽省安全人工智能重点实验室，安徽大学）；Anhui Provincial Engineering Research Center for Unmanned System（安徽省无人系统与智能技术工程研究中心）
本文提出一种用于工业异常检测的两阶段零样本异常分割框架，协同利用 CLIP 的异常定位与 SAM 的边界感知能力，通过 PPG 和 CPS 模块优化点提示与分割精度，在 Visa 等数据集上取得 SOTA 表现。
MadCLIP: Few-shot Medical Anomaly Detection with CLIP
Department of Computer Science, University of Verona（威尼斯大学计算机科学系）；AI for Good (AIGO) Research Unit, Istituto Italiano di Tecnologia（意大利技术研究院人工智能为善研究单元）
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）
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）
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC（中国科学技术大学生命科学与医学部生物医学工程学院）；MIRACLE Center, Suzhou Institute for Advance Research, USTC（中国科学技术大学苏州高等研究院MIRACLE中心）；Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS（中国科学院计算技术研究所中国科学院智能信息处理重点实验室）；State Key Laboratory of Precision and Intelligent Chemistry, USTC（中国科学技术大学精准与智能化学国家重点实验室）；Stanford University（斯坦福大学）
MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan)（计算能力网络与信息安全部教育部重点实验室，山东计算机科学中心（济南国家超级计算机中心））；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东科学院））；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science（山东省计算能力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）
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy（创新医学工程系，威尼斯大学，意大利）；School of Computer Science and Engineering, Beihang University, China（计算机科学与工程学院，北航大学，中国）；Dept. of Computer Science, Reykjavik University, Iceland（计算机科学系，雷克雅未克大学，冰岛）
