Video anomaly detection (VAD) is a challenging task that detects anomalous frames in continuous surveillance videos. Most previous work utilizes the spatio-temporal correlation of visual features to distinguish whether there are abnormalities in video snippets. Recently, some works attempt to introduce multi-modal information, like text feature, to enhance the results of video anomaly detection. However, these works merely incorporate text features into video snippets in a coarse manner, overlooking the significant amount of redundant information that may exist within the video snippets. Therefore, we propose to leverage the diversity among multi-modal information to further refine the extracted features, reducing the redundancy in visual features, and we propose Grained Multi-modal Feature for Video Anomaly Detection (GMFVAD). Specifically, we generate more grained multi-modal feature based on the video snippet, which summarizes the main content, and text features based on the captions of original video will be introduced to further enhance the visual features of highlighted portions. Experiments show that the proposed GMFVAD achieves state-of-the-art performance on four mainly datasets. Ablation experiments also validate that the improvement of GMFVAD is due to the reduction of redundant information.
DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Microsoft Research Asia（微软亚洲研究院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI
School of Computer Science and Technology, Hangzhou Dianzi University（计算机科学与技术学院，杭州电子科技大学）；School of Biomedical Engineering, Tsinghua University（生物医学工程学院，清华大学）；HDU-ITMO Joint Institute, Hangzhou Dianzi University（杭州电子科技大学-ITMO联合研究所）；School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China（合成过程工业系统自动化国家重点实验室，东北大学，沈阳110819，中华人民共和国）；School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia（软件与电子工程学院，斯威本科技大学，墨尔本，VIC 3122，澳大利亚）
Traffic anomaly detection has played a crucial role in Intelligent Transportation System (ITS). The main challenges of this task lie in the highly diversified anomaly scenes and variational lighting conditions. Although much work has managed to identify the anomaly in homogenous weather and scene, few resolved to cope with complex ones. In this paper, we proposed a dual-modality modularized methodology for the robust detection of abnormal vehicles. We introduced an integrated anomaly detection framework comprising the following modules: background modeling, vehicle tracking with detection, mas…
Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine（天津中医药大学中药制药工程学院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Guangzhou Institute of Technology, Xidian University（西安电子科技大学广州研究院）；State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine（天津中医药大学组分中药国家重点实验室）
Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection
Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能制造方向）；The Hong Kong University of Science and Technology（香港科技大学）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与车辆工程学院）
Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection
CNR-ISASI, Institute of Applied Sciences and Intelligent Systems / National Research Council of Italy（CNR-ISASI应用科学与智能系统研究所/意大利国家研究理事会）；CNR-STIIMA, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing（CNR-STIIMA智能工业技术与系统先进制造研究所）；Institute d'Electronique de Microelectronique et de Nanotechnologie (IEMN) / Universite Polytechnique Hauts de France（微电子与纳米技术研究所（IEMN）/法国高等法国理工学院）
School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computing and Information Technology, Great Bay University（海湾大学计算机与信息学院）
SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection
Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能制造学域）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；The Hong Kong University of Science and Technology（香港科技大学）
VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection
Department of Industrial Engineering, Tsinghua University, Beijing, China（清华大学工业工程系）；Lab, Huawei Technologies Ltd, Beijing, China（华为技术有限公司2012实验室）；Datadog AI Research（Datadog人工智能研究）
VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection
National Center of Technology Innovation for Intelligent Design and Numerical Control（国家智能设计与数控技术创新中心）；Huazhong University of Science and Technology（华中科技大学）
Towards Training-free Anomaly Detection with Vision and Language Foundation Models
Beihang University（北京航空航天大学）；State Key Laboratory of Complex and Critical Software Environment, Beihang University（北京航空航天大学复杂与关键软件环境国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data
Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University（江南大学江苏省模式识别与计算智能工程实验室）；College of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）
AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detection
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理学院计算与信息系统学院）；School of Artifical Intelligence, Xidian University（西安电子科技大学人工智能学院）
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Shenzhen Audencia Financial Technology Institute, Shenzhen University（深圳大学深圳Audencia金融科技研究院）；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）；CATL（宁德时代）；School of Computing and Artificial Intelligence, Fuyao University of Science and Technology（福耀科技大学计算与人工智能学院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）；Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）
Enhancing Weakly Supervised Multimodal Video Anomaly Detection through Text Guidance
School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）；Alibaba Cloud（阿里云）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
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）
BridgeNet: A Unified Multimodal Framework for Bridging 2D and 3D Industrial Anomaly Detection
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；State Key Lab of IOTSC, Department of CIS, University of Macau（物联网科学国家重点实验室，澳门大学）
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Huazhong University of Science and Technology（华中科技大学）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；Youtu Lab, Tencent（腾讯优图实验室）；Singapore Management University（新加坡管理大学）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth data in this context instead is still hardly explored in spite of depth images being a popular choice in many other computer vision research areas and the increasing availability of inexpensive depth camera hardware. We evaluate the application of existing autoencoder-based methods on depth video and propose how the advantages of using depth data can be leveraged by integration int…
AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学人工智能学院）；Objecteye Inc., Beijing, China（Objecteye公司）
University of Genova（热那亚大学）；University Carlos III de Madrid（马德里卡洛斯三世大学）
This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different v…
Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection
Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）；Shanghai Jiao Tong University（上海交通大学）；City University of Hong Kong（香港城市大学）；University of New South Wales（新南威尔士大学）；DZ Matrix（DZ矩阵）；Fudan University（复旦大学）；Rongcheer Co., Ltd.（荣彻科技有限公司）
Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learning
Mine Digitization Engineering Research Center of the Ministry of Education（教育部矿山数字化工程研究中心）；School of Computer Science and Technology, China University of Mining and Technology（中国矿业大学计算机科学与技术学院）；Sun Yueqi Honors College, China University of Mining and Technology（中国矿业大学孙越崎学院）
