We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent model of any particular scene. This model can be used to detect anomalies in new videos of the same scene. Importantly, our approach is explainable - our high-level appearance and motion features can provide human-understandable reasons for why any part of a video is classified as normal or anomalous. We conduct experiments on standard video anomaly detection datasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show significant improvements over the previous state-of-the-art.
EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；Department of Radiology, Mianyang Central Hospital（绵阳市中心医院放射科）；Center for Biomedical Imaging Research, Tsinghua University（清华大学生物医学成像研究中心）
Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）；School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
The aim of this work is to detect and automatically generate high-level explanations of anomalous events in video. Understanding the cause of an anomalous event is crucial as the required response is dependant on its nature and severity. Recent works typically use object or action classifier to detect and provide labels for anomalous events. However, this constrains detection systems to a finite set of known classes and prevents generalisation to unknown objects or behaviours. Here we show how to robustly detect anomalies without the use of object or action classifiers yet still recover the hi…
VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree
School of Software, Xi’an Jiaotong University（西安交通大学软件学院）；China Railway Xi’an Group（中国铁路西安集团）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices
University of Sheffield（谢菲尔德大学）；University College London（伦敦大学学院）；South London and Maudsley NHS Foundation Trust（南伦敦和莫兹利国民健康服务体系基金会信托）；NHS Foundation Trust（国民健康服务体系基金会信托）；Health Data Research UK London（英国健康数据研究伦敦中心）
Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples). One notorious issue with these approaches is that they are weak in discriminating anomalies from normal samples due to the lack of the knowledge about the anomalies. Here, we study the problem of few-shot anomaly detection, in which we aim at using a few labeled anomaly examples to train sample-efficient discriminative detection models. To address this problem, we introduce a novel weakly-supervised anomaly detection framework to train …
Technical University of Kaiserslautern（凯泽斯劳滕工业大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Technical University of Berlin（柏林工业大学）；Google Research, Brain Team（谷歌研究院大脑团队）；Korea University（高丽大学）
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common anomaly detection benchmarks with CIFAR-10 an…
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses problems for their acceptance, particularly with clinicians. Current explainable AI methods offer justifications through visualizations such as heat maps but cannot guarantee that the network is focusing on the relevant image region fully containing the anomaly. In this paper, we develop an approach to explainable AI in which the anomaly is assured to be overlapping the expected location when present. This is made p…
ManiVid: Unified and Explainable Forensic Analysis of Manipulated Videos
Shanghai Jiao Tong University（上海交通大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）
Semi-supervised video anomaly detection (VAD) methods formulate the task of anomaly detection as detection of deviations from the learned normal patterns. Previous works in the field (reconstruction or prediction-based methods) suffer from two drawbacks: 1) They focus on low-level features, and they (especially holistic approaches) do not effectively consider the object classes. 2) Object-centric approaches neglect some of the context information (such as location). To tackle these challenges, this paper proposes a novel two-stream object-aware VAD method that learns the normal appearance and …
