Anomaly detection (AD) in a surveillance scenario is an emerging and challenging field of research. For autonomous vehicles like drones or cars, it is immensely important to distinguish between normal and abnormal states in real-time. Additionally, we also need to detect any device malfunction. But the nature and degree of abnormality may vary depending upon the actual environment and adversary. As a result, it is impractical to model all cases a-priori and use supervised methods to classify. Also, an autonomous vehicle provides various data types like images and other analog or digital sensor data, all of which can be useful in anomaly detection if leveraged fruitfully. To that effect, in this paper, a heterogeneous system is proposed which estimates the degree of abnormality of an unmanned surveillance drone, analyzing real-time image and IMU (Inertial Measurement Unit) sensor data in an unsupervised manner. Here, we have demonstrated a Convolutional Neural Network (CNN) architecture, named AngleNet to estimate the angle between a normal image and another image under consideration, which provides us with a measure of anomaly of the device. Moreover, the IMU data are used in autoencoder to predict abnormality. Finally, the results from these two algorithms are ensembled to estimate the final degree of abnormality. The proposed method performs satisfactorily on the IEEE SP Cup-2020 dataset with an accuracy of 97.3%. Additionally, we have also tested this approach on an in-house dataset to validate its robustness.
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong Polytechnic University（香港理工大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Cornell University（康奈尔大学）；The Chinese University of Hong Kong（香港中文大学）
Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection
National Cancer Center Research Institute（国家癌症中心研究所）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）；Research Center for Advanced Science and Technology, The University of Tokyo（东京大学先进科学技术研究中心）；National Cancer Center Hospital（国家癌症中心医院）
Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；Purdue University（普渡大学）；Arima Inc.（Arima公司）；IQVIA（艾昆纬）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
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）
Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging
School of Electrical Engineering and Automation, Wuhan University（武汉大学电气与自动化学院）；University Hospital Heidelberg（海德堡大学医院）；Vitalent Consulting（维塔伦特咨询公司）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）；Uppsala University（乌普萨拉大学）；School of Computer Science, University of Sydney（悉尼大学计算机学院）
Faculty of Information Science and Engineering（信息科学与工程学院）；College of Computer Science and Engineering（计算机科学与工程学院）；Department of Computer Science and Engineering（计算机科学与工程系）；Department of Computer Science（计算机科学系）
Clinical Trajectory Alignment for Medical Vision-Language Pre-training
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Harvard University（哈佛大学）
University of Girona（赫罗纳大学）；Technical University of Munich（慕尼黑工业大学）；Antwerp University Hospital（安特卫普大学医院）；University of Antwerp（安特卫普大学）；KU Leuven（鲁汶大学）
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；Advanced Analytics Institute（高级分析研究所）；University of Technology Sydney（悉尼科技大学）
AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University（计算机科学与技术系，人工智能研究院，清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）；State Key Laboratory of Multimedia Information Processing, Peking University（多媒体信息处理国家重点实验室，北京大学）；School of Software Microelectronics, Peking University（软件微电子学院，北京大学）；Institute of Intelligent Computing, Alibaba Group（智能计算研究院，阿里巴巴集团）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室，上海，中国）；Jiangsu Collaborative Innovation Center for Language Competence, Jiangsu, China（江苏省语言能力协同创新中心，江苏，中国）；ModelTC Open Source Organization, Beijing, China（ModelTC开源组织，北京，中国）
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
Columbia University（哥伦比亚大学）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of Maryland, College Park（马里兰大学帕克分校）；University of California, Irvine（加州大学欧文分校）；University of Rochester（罗切斯特大学）；Universidade Federal de São Paulo（圣保罗联邦大学）；Duke University（杜克大学）；Emory University（埃默里大学）
Shallow Features Matter: Hierarchical Memory with Heterogeneous Interaction for Unsupervised Video Object Segmentation
Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University（上海智能信息处理重点实验室，计算机科学与人工智能学院，复旦大学）；The School of Computer Engineering and Science, Shanghai University（上海大学计算机工程与科学学院）
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions
Brainnetome Center, National Laboratory of Pattern Recognition (NLPR), CASIA（脑网络组中心，模式识别国家重点实验室，中国科学院自动化研究所）；Tencent Inc. China（腾讯公司（中国））；School of Future Technology, University of Chinese Academy of Sciences (UCAS)（中国科学院大学未来技术学院）
