MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Zero-shot anomaly detection (ZSAD) often leverages pretrained vision or vision-language models, but many existing methods use prompt learning or complex modeling to fit the data distribution, resulting in high training or inference cost and limited cross-domain stability. To address these limitations, we propose Memory-Retrieval Anomaly Detection method (MRAD), a unified framework that replaces parametric fitting with a direct memory retrieval. The train-free base model, MRAD-TF, freezes the CLIP image encoder and constructs a two-level memory bank (image-level and pixel-level) from auxiliary data, where feature-label pairs are explicitly stored as keys and values. During inference, anomaly scores are obtained directly by similarity retrieval over the memory bank. Based on the MRAD-TF, we further propose two lightweight variants as enhancements: (i) MRAD-FT fine-tunes the retrieval metric with two linear layers to enhance the discriminability between normal and anomaly; (ii) MRAD-CLIP injects the normal and anomalous region priors from the MRAD-FT as dynamic biases into CLIP's learnable text prompts, strengthening generalization to unseen categories. Across 16 industrial and medical datasets, the MRAD framework consistently demonstrates superior performance in anomaly classification and segmentation, under both train-free and training-based settings. Our work shows that fully leveraging the empirical distribution of raw data, rather than relying only on model fitting, can achieve stronger anomaly detection performance. The code will be publicly released at https://github.com/CROVO1026/MRAD.
Heterogeneous Vision-Language Ensemble with Disagreement-Aware Reranking for Text-Based Person Anomaly Retrieval
Hanoi University of Science and Technology（河内科技大学）；University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）；Vietnam National University, Ho Chi Minh City（胡志明市国家大学）；VinUniversity；Vietnamese-German University（越南德国大学）
In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However, we observe that the attention based knowledge retrieval mechanisms used in current NMNs restricts them from achieving their full potential as the attention process retrieves information based on a set of static connection weights. This is suboptimal in a setting where there are vast differences among samples in the data domain; such as anomaly det…
Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not only by the local context, but also according to the consistency between the testing event and the knowledge about normality from the training data. Concretely, we propose a novel two-stream framewor…
FaLCon: Facet-Anchored Retrieval with Late Consensus for Sim2Real Text-Based Person Anomaly Search
Vietnamese-German University（越南-德国大学）；Ho Chi Minh City University of Technology（胡志明市技术大学）；University of Information Technology（信息技术大学）；Ho Chi Minh city University of Science（胡志明市科学大学）
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval
Jiangxi Normal University（江西师范大学）；Zhejiang University（浙江大学）；Northwestern Polytechnical University（西北工业大学）；National University of Defense Technology（国防科技大学）
MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios
Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；Engineering Research Center of Cloud-Edge Intelligent Collaboration on Big Data, Ministry of Education, Beijing Normal University（北京师范大学大数据云边智能协同教育部工程研究中心）
Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院空天地一体化大数据应用技术国家工程实验室）；Guangzhou Institute of Technology, Xidian University（西安电子科技大学广州研究院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）
本文提出视频异常检索（VAR）新任务，构建 UCFCrime-AR 与 XDViolence-AR 基准，并设计 ALAN 模型实现长未裁剪视频的跨模态异常检索。
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）
TU Munich（慕尼黑工业大学）；Klinikum Rechts der Isar（Rechts der Isar 医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images. A plethora of such unsupervised anomaly detection approaches has been made in the medical domain, based on statistical methods, content-based retrieval, clustering and recently also deep learning. Previous approaches towards deep unsupervised anomaly detection model patches of normal anatomy with variants of Autoencoders or GANs, and detect anomalies either as outliers in the learned feature space or from large reconstruction errors. I…
BoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection
Department of Artificial Intelligence, FPT University（FPT大学人工智能系）；Department of IT, FPT University（FPT大学信息技术系）；Department of Computing Fundamental, FPT University（FPT大学计算基础系）
UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（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（中国科学院大学人工智能学院）；Yinwang Intelligent Technology Co. Ltd.（银河智能科技有限公司）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Wuhan AI Research（武汉人工智能研究所）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Intellectual Property & Big Data（广东省知识产权与大数据重点实验室）
Change detection, or anomaly detection, from street-view images acquired by an autonomous robot at multiple different times, is a major problem in robotic mapping and autonomous driving. Formulation as an image comparison task, which operates on a given pair of query and reference images is common to many existing approaches to this problem. Unfortunately, providing relevant reference images is not straightforward. In this paper, we propose a novel formulation for change detection, termed compressive change retrieval, which can operate on a query image and similar reference images retrieved fr…
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）
Anomaly Detection by Effectively Leveraging Synthetic Images
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Artificial Intelligence（人工智能系）；Department of Architecture（建筑系）；Department of Radiation Physics（放射物理系）
IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools
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）
