Unsupervised Anomaly Detection (UAD) aims to identify abnormal regions by establishing correspondences between test images and normal templates. Existing methods primarily rely on image reconstruction or template retrieval but face a fundamental challenge: matching between test images and normal templates inevitably introduces noise due to intra-class variations, imperfect correspondences, and limited templates. Observing that Retrieval-Augmented Generation (RAG) leverages retrieved samples directly in the generation process, we reinterpret UAD through this lens and introduce \textbf{RAID}, a retrieval-augmented UAD framework designed for noise-resilient anomaly detection and localization. Unlike standard RAG that enriches context or knowledge, we focus on using retrieved normal samples to guide noise suppression in anomaly map generation. RAID retrieves class-, semantic-, and instance-level representations from a hierarchical vector database, forming a coarse-to-fine pipeline. A matching cost volume correlates the input with retrieved exemplars, followed by a guided Mixture-of-Experts (MoE) network that leverages the retrieved samples to adaptively suppress matching noise and produce fine-grained anomaly maps. RAID achieves state-of-the-art performance across full-shot, few-shot, and multi-dataset settings on MVTec, VisA, MPDD, and BTAD benchmarks. \href{https://github.com/Mingxiu-Cai/RAID}{https://github.com/Mingxiu-Cai/RAID}.
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction
South China University of Technology（华南理工大学）；JD Finance America Corporation（京东金融美国公司）；Sun Yat-sen University（中山大学）
本文提出了一种将流重构和帧预测无缝结合的混合框架$\text{HF}^2$，用于视频异常检测。首先，设计了ML-MemAE-SC（Multi-Level Memory modules in an Autocoder with Skip Connections）网络，用于存储光流场重建的正常模式，从而能够灵敏地识别出具有较大重建误差的异常事件。更重要的是，在重建的光流的条件下，我们然后使用条件变分自编码器（CVAE），它捕捉视频帧和光流之间的高度相关性，以预测给定几个先前帧的下一帧。通过CVAE，血流重建的质量从本质上影响帧预测的质量。因此，异常事件的重构不佳的光流进一步恶化了最终预测的未来帧的质量，使得异常更可检测。通过仿真和实测数据处理验证了所提方法的有效性。代码可在\href{https://github.com/LiUzHiAn/hf2vad}{https://github.com/LiUzHiAn/hf2vad}上获得。
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）
IndusAgent: Agentic Reinforcement Learning for Cross-Category Industrial Anomaly Detection
State Key Lab of Processors, Institute of Computing Technology, CAS（处理器国家重点实验室，计算技术研究所，中国科学院）；Santa Clara University（圣克拉拉大学）；LongCat Team（LongCat团队）；Independent Researcher（独立研究者）；New York University（纽约大学）；Sun Yat-sen University（孙中山大学）；Nanyang Technological University（南洋理工大学）；Stanford University（斯坦福大学）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）
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）
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras
China Agricultural University（中国农业大学）；Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture（农业部农业信息获取技术重点实验室）；Beijing Engineering and Technology Research Center for Internet of Things in Agriculture（北京农业物联网工程技术研究中心）
Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imaging
Radiological Institute, University Hospital Erlangen (UKER)（放射医学研究所，埃尔兰根大学医院（UKER））；Institute of Information Processing, Leibniz University Hannover（信息处理研究所，汉诺威莱布尼茨大学）；Image Data Exploration and Analysis Lab, Friedrich-Alexander University Erlangen–Nürnberg (FAU)（图像数据探索与分析实验室，埃尔兰根-纽伦堡弗里德里希-亚历山大大学（FAU））；Institute of Women’s Health, UKER（妇女健康研究所，UKER）
Predictive Reasoning with Augmented Anomaly Contrastive Learning for Compositional Visual Relations
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；Institute of Biomedical Engineering, Ningbo institute of materials technology and engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所生物医学工程研究所）；School of Electrical & Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder
Harvard University（哈佛大学）；Singapore Management University（新加坡管理大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；University of Surrey（萨里大学）
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
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）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；Faculty of Health and Medical Sciences, University of Adelaide（阿德莱德大学健康与医学科学学院）
Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection
Nanyang Technological University（南洋理工大学）；Ant Digital Technologies, Ant Group（蚂蚁数字科技，蚂蚁集团）；Singapore University of Technology and Design（新加坡科技设计大学）；Singapore Management University（新加坡管理大学）
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University of Wuppertal（伍珀塔尔大学）；Interdisciplinary Center for Machine Learning and Data Analytics（机器学习与数据分析跨学科中心）
I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging
AI Research Lab, Department of Computer Science（人工智能研究实验室、计算机科学系）；Biomedical & Translational Sciences, Sanford School of Medicine University of South Dakota（生物医学与转化科学、桑福大学医学院）
Detecting Anomalies using Generative Adversarial Networks on Images
Birla Institute of Technology and Science, Pilani（皮拉尼比特拉技术与科学学院）；Durham University（杜伦大学）；Central Electronics & Engineering Research Institute（中央电子与工程研究所）
Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality
University of Biskra（比斯克拉大学）；University of Dubai（迪拜大学）；University of Technology Phra Nakhon（帕那空皇家理工大学）；College of Staten Island（史泰登岛学院）；Université Kasdi Merbah（卡斯迪·梅尔巴大学）
Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
Cambridge University Hospitals（剑桥大学医院）；Mitera Hospital（米特拉医院）；Radboud University Medical Center（拉德堡德大学医学中心）；University Hospital Aachen（亚琛大学医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Ribera Hospital（里贝拉医院）；Duke Breast Cancer MRI（杜克乳腺癌磁共振成像项目）
