Few-Shot Anomaly Detection

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Overview

Few-shot anomaly detection studies how to recognize unusual examples when only a few references—often normal samples, and sometimes labeled anomalies—are available. Common approaches compare pretrained visual or language features with support examples, build prototype or memory banks, model normality through reconstruction or subspaces, and adapt detectors with lightweight prompts or calibration. The methods are used in settings such as industrial defect inspection, medical imaging, graphs, audio, and time series, where collecting labels can be costly. A central challenge is making results transfer reliably across categories and distribution shifts while controlling reference contamination and setting trustworthy detection thresholds.

Latest papers

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40 papers6 papers in the last four weeks, up 20% on the four weeks before. 0.0% of all new papers.

  1. Do Words Matter? What Text Contributes to Few and Full-Shot Vision-Language Anomaly Detection

    Oct 25, 2026Arda Baris Basaran, Mesut Ceylan, Elgar Fleisch +1Multimodal Anomaly DetectionRepresentation Geometry in VLMs

    ETH Zurich · University of St. Gallen

  2. Flownomaly: Single-Step Conditional-Mean Recovery for Few-Shot Anomaly Detection

    Oct 23, 2026Bernardo Perrone Ribeiro, Erkut Akdag, Egor BondarevFew-Shot Anomaly DetectionIndustrial Anomaly Detection

    AIMS Group, Department of Electrical Engineering, Eindhoven University of Technology

  3. Few-Shot Adaptation for Anomalous Sound Detection at Low False-Positive Rate

    Oct 14, 2026Simone Lugani, Christof Pichler, Hannes Wegleiter +3Anomaly DetectionIndustrial Anomaly Detection

    DITEN, University of Genoa, Genoa, Italy · Christian Doppler Laboratory for Measurement Systems for Harsh Operating Conditions, EMS, TU Graz, Graz, Austria · voestalpine Stahl GmbH, Linz, Austria

  4. OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

    Oct 12, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +9Time-Series Anomaly DetectionAnomaly Detection

    LG AI Research

  5. Anchor and Adapt: Asymmetric Prompt Adaptation for Few-Shot Industrial Anomaly Detection

    Oct 4, 2026Mengyang Zhao, Teng Fu, Haiyang Yu +3Prompt Learning for VLMsFew-Shot Anomaly Detection

    Shanghai Key Lab of Intelligent Information Processing and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200433, China

  6. VD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detection

    Sep 28, 2026Mengyang Zhao, Zhuolin He, Haiyang Yu +9Few-Shot Anomaly DetectionVisual Reasoning

    Fudan University · University of Southern California · Fuzhou University +1

  7. The Selection Rule Decides the Winner: A Pre-Registered Audit of Open-Set Graph Anomaly Detection

    Sep 27, 2026Farhan Shahriyar Hossain, Taufikur Rahman Fuad, Md Abrar Jahin +1Few-Shot Anomaly DetectionBenchmark Auditing

    Islamic University of Technology · University of Southern California · Qatar Computing Research Institute (QCRI)

  8. How Many Categories Are Enough? Distribution-Free Certification Limits for Few-Shot Anomaly Thresholds

    Sep 23, 2026Gia Huy Thai, Nguyen Thai AnhFew-Shot Anomaly DetectionAnomaly Detection

    University of Science, VNU-HCM, Ho Chi Minh City, Vietnam · Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

  9. Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images

    Sep 11, 2026Juzheng Miao, Yuchen Yuan, Cheng Chen +1Few-Shot Anomaly DetectionMedical Image Anomaly Detection

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China · Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong, China · School of Biomedical Engineering, The University of Hong Kong, Hong Kong, China +1

  10. GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection

    Sep 4, 2026Xudong Wang, Chris Ding, Tongxin Li +1Few-Shot Anomaly DetectionGraph-Based Anomaly Detection

    School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China

  11. PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection

    Sep 3, 2026Xiaoyu Yang, Qixing Wu, Huixian Zhao +1Few-Shot Anomaly DetectionVision Foundation Model Adaptation

    School of Airspace Science and Engineering, Shandong University, Shandong, China

  12. DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection

    Aug 24, 2026Wenyang Liu, Tianyi Liu, Dongshuo Zhang +2Few-Shot Anomaly DetectionIndustrial Anomaly Detection

    Nanyang Technological University Singapore · Huazhong University of Science and Technology, Wuhan, China

  13. When More References Hurt: Contamination-Aware DINOv2 Memory Banks for Few-Shot Steel Defect Detection

    Aug 22, 2026Hannaneh Kalantary, Javad KhoramdelIndustrial Anomaly DetectionAnomaly Detection

    University of Padua · University of Vienna

  14. SPARC: Subspace Position-Aware Robust Few-Shot Calibration for Distribution-Shifted Industrial Anomaly Detection

    Aug 19, 2026Seokhee Han, Seungjun Chu, Mateusz Nowak +1Industrial Anomaly DetectionAnomaly Detection

    Korea University

  15. Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction

    Aug 18, 2026Ayusha Abbas, Saram Abbas, Kabita AdhikariAnomaly DetectionIndustrial Anomaly Detection

    School of Engineering, Newcastle University, Newcastle upon Tyne, UK

  16. ConceptADapt: Concept-guided Adaptive Feature Reconstruction with Dynamic Attention for Few-Shot Industrial Anomaly Detection

    Aug 6, 2026Yufei Li, Yicheng Ruan, Long Tian +2Few-Shot Anomaly DetectionIndustrial Anomaly Detection

    School of Computer Science and Technology, Xidian University, Xi’an, China

  17. PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images

    Aug 4, 2026Ruiqi Wang, Yiming Qian, Fenggen Yu +4Few-Shot Anomaly DetectionAnomaly Localization

    Amazon · Simon Fraser University · ⋆Work carried out during internship at Amazon. +1

  18. Beyond Static Anchors: Bounded Prototype Conditioning for Language-Free Medical Anomaly Detection

    Aug 1, 2026Yibo Wan, Jinyu Cai, See-kiong NgFew-Shot Anomaly DetectionMedical Image Anomaly Detection

    School of Computing, National University of Singapore, Singapore · Institute of Data Science, National University of Singapore, Singapore

  19. DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

    Jul 27, 2026Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang +2Few-Shot Anomaly DetectionAnomaly Localization

    Inventec Corporation, Taipei, Taiwan · University at Albany, State University of New York, NY, USA

  20. Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection

    Jul 14, 2026Jiaqi KuangFew-Shot Anomaly DetectionData Augmentation

    Oxford Suzhou Centre for Advanced Research, University of Oxford Suzhou, China

  21. Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound

    Jul 1, 2026Huanwen Liang, Yuhao Huang, Xiliang Zhu +6Few-Shot Anomaly DetectionPrototype-Based Classification

    Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China

  22. UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction

    Jun 29, 2026Zhaopeng Gu, Bingke Zhu, Zhaowen Li +5Few-Shot Anomaly DetectionAnomaly Localization

    Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · Yinwang Intelligent Technology Co. Ltd., China +4

  23. Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection

    Jun 20, 2026Weizhi Nie, Weichao Liu, Weijie Wang +1Few-Shot Anomaly DetectionWorld Model Learning

    Tianjin University

  24. CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection

    Jun 18, 2026Junhao Cai, Junyu Chen, Deyu Zeng +4Few-Shot Anomaly DetectionMultimodal Anomaly Detection

    Shenzhen University, Shenzhen, Guangdong 518060, China · Guangzhou Maritime University, Guangzhou, Guangdong 510725, China · Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong 511453, China

  25. VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignment

    Jun 3, 2026Zi Wang, Katsuya Hotta, Yawen Zou +4Point Cloud Anomaly DetectionFew-Shot Anomaly Detection

    Niigata University, Niigata-city, Japan · Iwate University, Morioka, Japan · University of Toyama, Toyama-city, Japan

  26. AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection

    May 28, 2026Yi Zhang, Jiawen Zhu, Lele Fu +1Few-Shot Anomaly DetectionTool Use in VLMs

    Singapore Management University, Singapore · Sun Yat-sen University, China

  27. Beyond Normal References: Discriminative Few-Shot Anomaly Detection

    May 22, 2026Huan Wang, Jun Shen, Jun Yan +1Few-Shot Anomaly DetectionFew-Shot Learning

    Singapore Management University, Singapore · University of Wollongong, Australia

  28. Res2^2CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment

    May 15, 2026Xinyue Liu, Jianyuan Wang, Biao Leng +1Few-Shot Anomaly DetectionVLM Adaptation

    Beihang University · University of Science and Technology Beijing · Beijing Jiaotong University

  29. Hypergraph-Enhanced Training-Free and Language-Free Few-Shot Anomaly Detection

    May 11, 2026Guohuan Xie, Xin He, Dingying Fan +2Few-Shot Anomaly DetectionMedical Image Anomaly Detection

    Nankai University · Tianjin University of Technology · Tsinghua University

  30. H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection

    Apr 16, 2026Jianghong Huang, Luping Ji, Weiwei Duan +1Few-Shot Anomaly DetectionMedical Image Anomaly Detection

    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China