Weakly-supervised anomaly detection for multimodal data distributions
School of Marine Science and Technology（海洋科学与技术学院）；Northwestern Polytechnical University（西北工业大学）；School of Computing（计算机学院）；University of Eastern Finland（东芬兰大学）；University of Turku（图尔库大学）；Singapore Institute of Technology（新加坡理工学院）
Weakly-supervised anomaly detection can outperform existing unsupervised methods with the assistance of a very small number of labeled anomalies, which attracts increasing attention from researchers. However, existing weakly-supervised anomaly detection methods are limited as these methods do not factor in the multimodel nature of the real-world data distribution. To mitigate this, we propose the Weakly-supervised Variational-mixture-model-based Anomaly Detector (WVAD). WVAD excels in multimodal datasets. It consists of two components: a deep variational mixture model, and an anomaly score estimator. The deep variational mixture model captures various features of the data from different clusters, then these features are delivered to the anomaly score estimator to assess the anomaly levels. Experimental results on three real-world datasets demonstrate WVAD's superiority.
RobustA: Robust Anomaly Detection in Multimodal Data
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；IMT School for Advanced Studies（IMT高级研究学院）；Johannes Kepler University Linz（林茨约瑟夫·冯·克莱门茨大学）；Human-centered AI Group, AI Lab, Linz Institute of Technology（以人为中心的人工智能小组、人工智能实验室、林茨技术研究所）
UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU)（化学工程系，挪威科学与技术大学）；Department of Computing, Imperial College London（计算系，帝国理工学院伦敦分校）
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；CreateLink Technology（创联科技）
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Sichuan University（四川大学）；University of Connecticut（康涅狄格大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Adelaide（阿德莱德大学）
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Aalborg University（奥尔堡大学）；Pioneer Centre for Artificial Intelligence（先锋人工智能中心）；Technical University of Denmark（丹麦技术大学）；Milestone Systems（Milestone系统）；University of Bucharest（布加勒斯特大学）；University of Surrey（萨里大学）
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）
NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines
Artificial Intelligence Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；Clemson Composites Center, Clemson University（克莱姆森大学复合材料中心）；Intelligent Circuits, Architectures and Systems Lab, University of South Carolina（南卡罗来纳大学智能电路、架构和系统实验室）
RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants
Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）；Chongqing University（重庆大学）
Shanghai University of Finance and Economics（上海财经大学）；University of Science and Technology of China（中国科学技术大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Carnegie Mellon University（卡内基梅隆大学）
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
School of Software and Microelectronics Peking University（北京大学软件与微电子学院）；School of Computer Science Peking University（北京大学计算机学院）；National Engineering Center of Software Engineering Peking University（北京大学软件工程国家工程研究中心）
TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Network
University of Arkansas（阿肯色大学）；University of Science, Vietnam National University（越南国家大学科学大学）；John von Neumann Institute, Vietnam National University（越南国家大学约翰·冯·诺依曼研究所）；West Virginia University（西弗吉尼亚大学）
Weakly-Supervised Anomaly Detection in the Milky Way
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Rutgers University（罗格斯大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Bosch Research North America（博世北美研究院）
TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches
Département de Physique Nucléaire et Corpusculaire, University of Geneva（核物理与粒子物理系，日内瓦大学）；Department of Computer Science, University of Geneva（计算机科学系，日内瓦大学）
Ecole Normale Supérieure（巴黎高等师范学院）；Ecole des Hautes Etudes en Sciences Sociales（巴黎高等社会科学研究学院）；PSL Research University（PSL研究大学）；Centre National de la Recherche Scientifique（法国国家科学研究中心）；Facebook AI Research（脸书人工智能研究院）
Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework
Department of Computer Science, University of Maryland, Baltimore County（计算机科学系，马里兰大学巴尔的摩县分校）；Department of Dermatology, Johns Hopkins University School of Medicine（皮肤科系，约翰霍普金斯大学医学院）
