Multimodal Anomaly Detection

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7 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

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Latest papers 54

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DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models
Jun 15, 2026Dingrong Wang, Xian Tao, Zhen Qu +5Reinforcement Learning Industrial Anomaly Detection
Institute of Automation, Chinese Academy of Sciences (CAS), Beijing 100190, China · School of Artificial Intelligence, University of Chinese Sciences, Beijing 100049, China · Institute of Automation, CAS, Beijing 100190, China +4
DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN
Jun 4, 2026Francesco Spinelli, Esteban Municio, Pau Baguer +2Wireless Communications Open Radio Access Network
i2CAT Foundation · NEC Laboratories Europe · ICREA
A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision
Jun 1, 2026Stefano Samele, Eugenio Lomurno, Teodora Jovanovic +3VLM Evaluation Industrial Anomaly Detection
Politecnico di Milano, AIRLab, Milan, Italy · S&H – Software & Hardware, Milan, Italy
DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection
May 30, 2026Arun SharmaRemote Sensing Image Understanding Multimodal Anomaly Detection
University of Minnesota, Twin Cities
AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection
May 28, 2026Yi Zhang, Jiawen Zhu, Lele Fu +1Few-Shot Anomaly Detection Tool Use in VLMs
Singapore Management University, Singapore · Sun Yat-sen University, China
Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection
May 28, 2026Yangchen Wu, Huiqiang XieIndustrial Anomaly Detection Anomaly Localization
School of Information Science and Technology, Jinan University, Guangzhou, Guangdong 510632, P. R. China
An Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?
May 25, 2026Sangeeta ., Maddikuntla Sai Prajwal, Debi Prosad Dogra +4Multimodal Anomaly Detection Video Anomaly Detection
Indian Institute of Technology Bhubaneswar Bhubaneswar, Odisha 752050, India · Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology Seoul 02792, Republic of Korea · Yonsei-KIST Convergence Research Institute, Yonsei University Seoul 03722, Republic of Korea
IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools
May 20, 2026Rongbin Tan, Fangfang Lin, Zhenlong Yuan +10Industrial Anomaly Detection Tool-Using Vision-Language Agents
State Key Lab of Processors, Institute of Computing Technology, CAS
JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA
May 19, 2026Hyunju Kang, Woohyun Lee, Jaewon Kim +1Industrial Anomaly Detection Anomaly Localization
Sungkyunkwan University · Seoul National University
UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
May 18, 2026Robson W. S. Pessoa, Julien Amblard, Alessandra Russo +1Multivariate Time Series Forecasting Industrial Anomaly Detection
aDepartment of Chemical Engineering, Norwegian University of Science and Technology2026 (NTNU), Trondheim, Norway · bDepartment of Computing, Imperial College London, London, United Kingdom
Res $^2$ CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment
May 15, 2026Xinyue Liu, Jianyuan Wang, Biao Leng +1Few-Shot Anomaly Detection VLM Adaptation
Beihang University · University of Science and Technology Beijing · Beijing Jiaotong University
Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
May 15, 2026Heqiang Wang, Weihong Yang, Zheyuan Yang +4Industrial Anomaly Detection Multimodal Anomaly Detection
Pengcheng Laboratory, Shenzhen, 518066, China · Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection
May 14, 2026Mitchell Piehl, Muchao YeInterpretable Anomaly Detection VLM Reasoning
The University of Iowa, Iowa City IA 52245, USA
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
May 12, 2026Muhammad Aqeel, Maham Nazir, Uzair Khan +2VLM Adaptation Medical Image Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy · School of Computer Science and Engineering, Beihang University, China · Dept. of Engineering for Innovation +1
MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection
May 11, 2026Xiran Zhao, Jing Jin, Yan Bai +6Multi-View Learning Industrial Anomaly Detection
ShanghaiTech University · Tsinghua University · Meituan Inc. +1
Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection
May 8, 2026Wenbing Zhu, Jianing Liang, Linjie Cheng +7Industrial Anomaly Detection Multimodal Anomaly Detection
1Fudan University · 4Rongcheer Co., Ltd. · 3Donghua University +1
Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection
May 7, 2026Letian Bai, Xuanming Cao, Juan Du +1Prompt Learning Cross-Modal Alignment
Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology · College of Mechanical and Vehicle Engineering, Hunan University
Text-Guided Multimodal Unified Industrial Anomaly Detection
Apr 24, 2026Zewen Li, Shuo Ye, Zitong Yu +2Unified Multimodal Models Industrial Anomaly Detection
School of Computer Science & Software Engineering, Shenzhen University, China, 518060 · School of Computing and Information Technology, Great Bay University, Dongguan, 523000,China
ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
Apr 20, 2026Qiuhui Chen, Jiaxiang Song, Shuai Tan +1Multimodal Grounding Interpretable Anomaly Detection
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
MedAD-R1: Consistency-Reinforced Policy Optimization for Interpretable Medical Anomaly Detection
Feb 1, 2026Haitao Zhang, Yingying Wang, Jiaxiang Wang +5Healthcare Group Relative Policy Optimization
School of Informatics, Xiamen University · Institute of Artificial Intelligence, Xiamen University · Zhejiang Expressway Co., Ltd. +3
Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
Dec 10, 2025Nadeem Nazer, Hongkuan Zhou, Lavdim Halilaj +2Industrial Anomaly Detection Multimodal Anomaly Detection
Corporate Research, Robert Bosch GmbH, Renningen, Germany · Otto-von-Guericke-University Magdeburg, Magdeburg, Germany · Institute for Artificial Intelligence, University of Stuttgart, Stuttgart, Germany +2
AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
Aug 6, 2025Jingyi Liao, Yongyi Su, Rong-Cheng Tu +6VLM Adaptation Industrial Anomaly Detection
1Nanyang Technological University · Institute for Infocomm Research (I2R), A*STAR · 3South China University of Technology
Uncertainty-Weighted Fusion of Image and Synthetic Event for Video Anomaly Detection
May 5, 2025Sungheon Jeong, Jihong Park, Mohsen ImaniUncertainty-Aware Multimodal Fusion Multimodal Anomaly Detection
University of California, Irvine · MOLOCO
Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners
Dec 12, 2024Xinyue Liu, Jianyuan Wang, Biao Leng +1Cross-Modal Knowledge Distillation Industrial Anomaly Detection
School of Computer Science and Engineering, Beihang University, Beijing 100191, China · Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, and the School of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China · School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China

Jun 15, 2026, cs.CVJ/K move · Enter open · S save

DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models

Dingrong Wang, Xian Tao, Zhen Qu, Hengliang Luo+4

Institute of Automation, Chinese Academy of Sciences (CAS), Beijing 100190, China · School of Artificial Intelligence, University of Chinese Sciences, Beijing 100049, China · Institute of Automation, CAS, Beijing 100190, China+4

Reinforcement Learning Industrial Anomaly Detection Multimodal Large Language Models Multimodal Anomaly Detection Visual Grounding

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Industrial anomaly localization aims to accurately identify and localize abnormal regions in industrial products, addressing the critical challenge of detecting unseen defect categories in real-world scenarios. Traditional closed-set methods often suffer from poor cross-scenario generalization, while existingMultimodal Large Language Model (MLLM)-based approachesface two core limitations: they either adopt QA-style paradigmsmisaligned with the practical demands of localization, or relyon standard optimization techniques such as Group RelativePolicy Optimization (GRPO), which fails to deliver effectivelearning signals for subtle defects. To tackle these issues, thispaper proposes DifferAD-R1, an MLLM-augmented reinforcement learning framework tailored for industrial anomaly localization. We design a Difference-Guided dual-image paradigm,which reformulates the localization task as a one-shot difference grounding problem to effectively explore cross-scenarioanomalies. A Dual-Consistency Localization Reward is developedfor hard-to-detect anomalies, enhancing optimization stabilityand robustness. Additionally, we integrate a difficulty-awarestrategy with adaptive reweighting and group-wise resamplingto prioritize learning on challenging instances. To facilitateevaluations in real-world industrial settings, we construct theAD-DualDiff dataset, comprising 13K paired images across 20categories. Experimental results demonstrate that DifferADR1 significantly outperforms existing baselines and achievescompetitive performance compared to large-scale models likeQwen3-VL (235B parameters). Our code is publicly availableat: https://github.com/Rong2026/work-1.

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