mlsanityData to 7 Oct 2026

Registration based Few-Shot Anomaly Detection

arXiv 2207.07361 ↗First posted 15 Jul 2022cs.CVcited by 133

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Abstract (arXiv)

This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided for each category at training. So far, existing FSAD studies follow the one-model-per-category learning paradigm used for standard AD, and the inter-category commonality has not been explored. Inspired by how humans detect anomalies, i.e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model. During testing, the anomalies are identified by comparing the registered features of the test image and its corresponding support (normal) images. As far as we know, this is the first FSAD method that trains a single generalizable model and requires no re-training or parameter fine-tuning for new categories. Experimental results have shown that the proposed method outperforms the state-of-the-art FSAD methods by 3%-8% in AUC on the MVTec and MPDD benchmarks.

Key facts. Cited by 133 papers in the map (data through 7 Oct 2026). Builds on 27 of its 47 references that are in the map. First posted on arXiv 15 Jul 2022.

Builds on

27 of 47 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  5. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  6. 2015Spatial Transformer Networkscited by 1,423
  7. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  8. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  9. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  10. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  11. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  12. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
15 more
  1. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  2. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  3. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  4. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  5. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  6. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  7. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  8. 2021Free Lunch for Few-shot Learning: Distribution Calibrationcited by 165
  9. 2020Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspectivecited by 120
  10. 2018Multi-level Semantic Feature Augmentation for One-shot Learningcited by 62
  11. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  12. 2022Self-Supervised Masking for Unsupervised Anomaly Detection and Localizationcited by 43
  13. 2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
  14. 2016Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysiscited by 13
  15. 2020ESAD: End-to-end Deep Semi-supervised Anomaly Detectioncited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 27; 2024: 41; 2025: 40; 2026: 23.

133 papers.

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  1. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  2. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  3. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  4. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  5. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  6. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 126
  7. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  8. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 97
  9. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 92
  10. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  11. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  12. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
38 more
  1. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  2. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  3. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  4. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  5. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
  6. 2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 47
  7. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  8. 2023Diversity-Measurable Anomaly Detectioncited by 41
  9. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  10. 2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35
  11. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  12. 2022MAEDAY: MAE for few and zero shot AnomalY-Detectioncited by 30
  13. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  14. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 28
  15. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  16. 2024ResAD: A Simple Framework for Class Generalizable Anomaly Detectioncited by 24
  17. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  18. 2023PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentationcited by 21
  19. 2024Towards Generalizable Scene Change Detectioncited by 17
  20. 2023Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detectioncited by 15
  21. 2024Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detectioncited by 15
  22. 2024Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detectioncited by 15
  23. 2024Long-Tailed Anomaly Detection with Learnable Class Namescited by 14
  24. 2023LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detectioncited by 13
  25. 2024COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detectioncited by 13
  26. 2024AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localizationcited by 12
  27. 2025MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learningcited by 12
  28. 2025MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learningcited by 12
  29. 2023Combating Representation Learning Disparity with Geometric Harmonizationcited by 11
  30. 2024CSAD: Unsupervised Component Segmentation for Logical Anomaly Detectioncited by 11
  31. 2023Optimizing PatchCore for Few/many-shot Anomaly Detectioncited by 10
  32. 2023Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localizationcited by 10
  33. 2025MadCLIP: Few-shot Medical Anomaly Detection with CLIPcited by 10
  34. 2025Normal-Abnormal Guided Generalist Anomaly Detectioncited by 10
  35. 2025DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookupcited by 9
  36. 2023FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networkscited by 8
  37. 2023What makes a good data augmentation for few-shot unsupervised image anomaly detection?cited by 8
  38. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8

50 most cited of 133 All 133 (JSON)