Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly supervised anomaly detection, and take maximum advantage of these well-developed classifiers. For this purpose, we devise a graph convolutional network to correct noisy labels. Based upon feature similarity and temporal consistency, our network propagates supervisory signals from high-confidence snippets to low-confidence ones. In this manner, the network is capable of providing cleaned supervision for action classifiers. During the test phase, we only need to obtain snippet-wise predictions from the action classifier without any extra post-processing. Extensive experiments on 3 datasets at different scales with 2 types of action classifiers demonstrate the efficacy of our method. Remarkably, we obtain the frame-level AUC score of 82.12% on UCF-Crime.
Anomaly detection in crowds enables early rescue response. A plug-and-play smart camera for crowd surveillance has numerous constraints different from typical anomaly detection: the training data cannot be used iteratively; there are no training labels; and training and classification needs to be performed simultaneously. We tackle all these constraints with our approach in this paper. We propose a Core Anomaly-Detection (CAD) neural network which learns the motion behavior of objects in the scene with an unsupervised method. On average over standard datasets, CAD with a single epoch of traini…
Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention
organization= School of Aeronautics；Astronautics , addressline= Zhejiang University , city= Hangzhou , postcode= 310027 , country= China；organization= School of Computer Science；organization= Ningbo Innovation Center , addressline= Zhejiang University , city= Ningbo , country= China；organization= Department of Automation , addressline= Tsinghua University , city= Beijing , postcode= 100084 , country= China；organization= College of Information Science \& Electronic Engineering , addressline= Zhejiang University , city= Hangzhou , postcode= 310027 , country= China
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）
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom anomaly detection algorithms via a plug-and-play approach. Anomalib comprises state-of-the-art anomaly detection algorithms that achieve top performance on the benchmarks and that can be used off-the-shelf. In addition, the library provides components to design custom algorithms that could be tailored towards specific needs. Additional tools, includin…
SenseTime Research（商汤科技研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Unsupervised anomaly detection and localization is crucial to the practical application when collecting and labeling sufficient anomaly data is infeasible. Most existing representation-based approaches extract normal image features with a deep convolutional neural network and characterize the corresponding distribution through non-parametric distribution estimation methods. The anomaly score is calculated by measuring the distance between the feature of the test image and the estimated distribution. However, current methods can not effectively map image features to a tractable base distributio…
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）
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
Faculty of Computing, Harbin Institute of Technology, Harbin, China University of New South Wales, Sydney, Australia School of Biomedical Engineering, Tsinghua University, Beijing, China School of Artificial Intelligence；Robotics, Hunan University, Changsha, China Big data institute, Central South University, Sydney, Australia DZ-Matrix, Beijing, China Chongqing Research Institute of HIT, Chongqing, China
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection
Shanghai Institute of Intelligent Science and Technology, College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院上海智能科学技术研究院）
本文提出零样本即插即用框架 SAM-LAD，结合 SAM、对象匹配、DCGA 与异常度量模型，在多个工业和逻辑数据集上实现优于现有 SoTA 的逻辑及结构异常检测。
RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation
Institute of Trustworthy Embodied AI（可信具身人工智能研究院）；Shanghai Key Laboratory of Multimodal Embodied AI（上海多模态具身人工智能重点实验室）；Singapore Management University（新加坡国立大学）
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China（合成过程工业综合自动化国家重点实验室，东北大学，沈阳，中国）；Meta, London, U.K.（Meta，伦敦，英国）；Surrey Institute for People-Centred Artificial Intelligence（以人为本的人工智能研究院）
Anomaly detection in videos has been attracting an increasing amount of attention. Despite the competitive performance of recent methods on benchmark datasets, they typically lack desirable features such as modularity, cross-domain adaptivity, interpretability, and real-time anomalous event detection. Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly detection. Motivated by these research gaps, we propose a modular and unified approach to th…
Australian National University（澳大利亚国立大学）；Data61-CSIRO（联邦科学与工业研究组织Data61）；Monash University（莫纳什大学）
Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature. However, the success of the geometry constraints, posed in the embedding space, indicates that curved spaces might encode more structural information, leading to better discriminative power and hence richer representations. In this work, we investigate benefits of the curved space for analyzing anomalies or out-of-distribution objects in data. This is achieved by considering embeddings via three geometry constraints, namely, spherical geometry (with …
OoDDINO:A Multi-level Framework for Anomaly Segmentation on Complex Road Scenes
College of Computer Science and Artificial Intelligence, Southwest Minzu University（计算机科学与人工智能学院，西南民族大学）；Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, China（可持续城市智能交通工程研究中心，教育部，中国）
Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Nanjing University of Science and Technology（南京理工大学）
The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets are susceptible to unknown anomalies in complex clinical environments. In particular, the high false positive rate of polyp detection remains a major challenge in clinical practice. In this work, we release the FPPD-13 dataset, which provides a taxonomy and real-world cases of typical false positives during computer-aided polyp detection in real-world colonoscopy. We further propose a post-hoc module EndoBoost, whic…
The University of Tokyo（东京大学）；National Institute of Infomatics（国立信息学研究所）；Microsoft Research Asia（微软亚洲研究院）
This paper addresses the problem of joint detection and recounting of abnormal events in videos. Recounting of abnormal events, i.e., explaining why they are judged to be abnormal, is an unexplored but critical task in video surveillance, because it helps human observers quickly judge if they are false alarms or not. To describe the events in the human-understandable form for event recounting, learning generic knowledge about visual concepts (e.g., object and action) is crucial. Although convolutional neural networks (CNNs) have achieved promising results in learning such concepts, it remains …
Hefei University of Technology（合肥工业大学）；Jianghuai Advance Technology Center（江淮先进技术中心）；Anhui Provincial Key Laboratory of Humanoid Robots（安徽省人形机器人重点实验室）；Kaiyang Laboratory, Chery（凯阳实验室，中国奇瑞）；Zhejiang University of Technology（浙江工业大学）
