Video anomaly detection refers to the identification of events that deviate from the expected behavior. Due to the lack of anomalous samples in training, video anomaly detection becomes a very challenging task. Existing methods almost follow a reconstruction or future frame prediction mode. However, these methods ignore the consistency between appearance and motion information of samples, which limits their anomaly detection performance. Anomalies only occur in the moving foreground of surveillance videos, so the semantics expressed by video frame sequences and optical flow without background information in anomaly detection should be highly consistent and significant for anomaly detection. Based on this idea, we propose Appearance-Motion Semantics Representation Consistency (AMSRC), a framework that uses normal data's appearance and motion semantic representation consistency to handle anomaly detection. Firstly, we design a two-stream encoder to encode the appearance and motion information representations of normal samples and introduce constraints to further enhance the consistency of the feature semantics between appearance and motion information of normal samples so that abnormal samples with low consistency appearance and motion feature representation can be identified. Moreover, the lower consistency of appearance and motion features of anomalous samples can be used to generate predicted frames with larger reconstruction error, which makes anomalies easier to spot. Experimental results demonstrate the effectiveness of the proposed method.
Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detection
Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology（陕西网络计算与安全技术重点实验室，西安理工大学计算机科学与工程学院）；School of Cyber Science and Engineering, Xi’an Jiaotong University（网络安全与工程学院，西安交通大学）
ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection
Tsinghua University Shenzhen International Graduate School（清华大学深圳国际研究生院）；Ratel Soft；Affiliated Fifth Hospital, Wenzhou Medical University（温州医科大学附属第五医院）；Chinese Medicine Guangdong Laboratory（广东中医药大学实验室）
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）
VADMamba++: Efficient Video Anomaly Detection via Hybrid Modeling in Grayscale Space
Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology（陕西省网络计算与安全技术重点实验室，西安理工大学计算机科学与工程学院）
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）
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）
Beyond Geometry: Benchmarking and Consistency Reasoning for 3D Logical Anomaly Detection
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks
Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Fraunhofer Institute for Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光电子、系统技术与图像利用研究所（IOSB））
State Beyond Appearance: Diagnosing and Improving State Consistency in Dial-Based Measurement Reading
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京先进创新中心）；Beihang University（北航）；Fudan University（复旦大学）；Peking University（北京大学）
MedAD-R1: Consistency-Reinforced Policy Optimization for Interpretable Medical Anomaly Detection
School of Informatics, Xiamen University（厦门大学信息学院）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；Zhejiang Expressway Co., Ltd.（浙江高速公路有限公司）；School of Science and Engineering, Chinese University of Hong Kong（香港中文大学科学与工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
