Open-set supervised anomaly detection (OSAD) aims to identify unseen anomalies using limited anomalous supervision. However, existing prototype-based methods typically model normal data via a unimodal Gaussian prior, failing to capture inherent multi-modality and resulting in blurred decision boundaries. To address this, we propose Mixture Prototype Flow Matching (MPFM), a framework that learns a continuous transformation from normal feature distributions to a structured Gaussian mixture prototype space. Departing from traditional flow-based approaches that rely on a single velocity vector, MPFM explicitly models the velocity field as a Gaussian mixture prior where each component corresponds to a distinct normal class. This design facilitates mode-aware and semantically coherent distribution transport. Furthermore, we introduce a Mutual Information Maximization Regularizer (MIMR) to prevent prototype collapse and maximize normal-anomaly separability. Extensive experiments demonstrate that MPFM achieves state-of-the-art performance across diverse benchmarks under both single- and multi-anomaly settings.
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Intelligent Manufacturing, Wuxi Vocational College of Science and Technology（无锡科技职业学院智能制造学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院、教育部人工智能重点实验室）
Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；School of Medicine, Yale University（耶鲁大学医学院）；Boston Children’s Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Eli Lilly and Company（礼来公司）
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；State Key Laboratory of Submarine Geoscience, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学海洋地质国家重点实验室、自动化与智能传感学院）
Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection
School of Mathematics, Shandong University（山东大学数学学院）；Department of Interventional Medicine and Minimally Invasive Oncology, The Second Qilu Hospital of Shandong University（山东大学第二齐鲁医院介入医学与微创肿瘤科）；Department of Neurosurgery, The Second Qilu Hospital of Shandong University（山东大学第二齐鲁医院神经外科）
Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection
School of Computer Science, Wuhan University（武汉大学计算机科学学院）；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））；School of Information and Software Engineering, East China Jiaotong University（华东交通大学信息工程学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
BoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection
Department of Artificial Intelligence, FPT University（FPT大学人工智能系）；Department of IT, FPT University（FPT大学信息技术系）；Department of Computing Fundamental, FPT University（FPT大学计算基础系）
Brain-inspired analogical mixture prototypes for few-shot class-incremental learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
NepAl Applied Mathematics and Informatics Institute（尼泊尔应用数学与信息学研究所）；Fogsphere (Redev.AI Ltd)（福格球（Redev.AI有限公司））；University of Lausanne（洛桑大学）；West Virginia University（西弗吉尼亚大学）；University of Verona（维罗纳大学）；University College London（伦敦大学学院）；University of Aberdeen（阿伯丁大学）
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition
Australian Institute for Machine Learning (AIML), University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Stanford University（斯坦福大学）；Cornell University（康奈尔大学）；Cornell Tech（康奈尔理工学院）；University of Oxford（牛津大学）；St. Vincent’s Institute of Medical Research（圣文森特医学研究所）；Melbourne Integrative Genomics, The University of Melbourne（墨尔本大学墨尔本整合基因组学中心）；St Vincent’s Hospital Melbourne（墨尔本圣文森特医院）；Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（萨里大学视觉、语音与信号处理中心）；The University of Adelaide（阿德莱德大学）
Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detection
School of Information Engineering（信息工程学院）；Suqian University（宿迁大学）；School of Electronic & Information Engineering（电子与信息工程学院）；Nanjing University of Information Science and Technology（南京信息科学技术大学）；University of Electronic Science and Technology of China（电子科学与技术大学）；Institute for Advanced Studies（高级研究机构）；Universiti Malaya（马来亚大学）
Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound
Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University（深圳大学医学超声图像计算实验室）；School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学部生物医学工程学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人创新中心）；Shenzhen Luohu People’s Hospital (The Third Affiliated Hospital of Shenzhen University)（深圳市罗湖区人民医院（深圳大学第三附属医院））；Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Nanjing Medical University（南京医科大学附属苏州医院（苏州市立医院）医学超声中心）；Northwest Women’s and Children’s Hospital（西北妇女儿童医院）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation
University of Virginia（弗吉尼亚大学）；Cardiovascular Research Center, University of Virginia（弗吉尼亚大学心血管研究中心）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）
Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件学院）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Department of Precision Instrument, Tsinghua University（清华大学精密仪器系）；School of Mechanical Science & Engineering, Huazhong University of Science & Technology（华中科技大学机械科学与工程学院）
GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection
Tsinghua University Shenzhen International Graduate School（清华大学深圳国际研究生院）；Ratel Soft；Affiliated Fifth Hospital, Wenzhou Medical University（温州医学院附属第五医院）；Chinese Medicine Guangdong Laboratory（广东中医药实验室）
INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University（清华大学精密测量技术与仪器国家重点实验室，精密仪器系）；State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学智能制造装备技术国家重点实验室）；Hunan University School of Robotics（湖南大学机器人学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
AnomExpert: Identifying and Selecting Anatomical Planes for Prenatal Ultrasound Anomaly Diagnosis
Medical Ultrasound Image Computing (MUSIC) Lab, School of Artificial Intelligence, Shenzhen University（医学超声图像计算（MUSIC）实验室，深圳大学人工智能学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）
