mlsanityData to 7 Oct 2026

Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore

arXiv 2301.12082 ↗First posted 28 Jan 2023cs.CVcited by 75

Not on the map. Why?

Abstract (arXiv)

In the area of fewshot anomaly detection (FSAD), efficient visual feature plays an essential role in memory bank M-based methods. However, these methods do not account for the relationship between the visual feature and its rotated visual feature, drastically limiting the anomaly detection performance. To push the limits, we reveal that rotation-invariant feature property has a significant impact in industrial-based FSAD. Specifically, we utilize graph representation in FSAD and provide a novel visual isometric invariant feature (VIIF) as anomaly measurement feature. As a result, VIIF can robustly improve the anomaly discriminating ability and can further reduce the size of redundant features stored in M by a large amount. Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection. A comprehensive evaluation is provided for comparing GraphCore and other SOTA anomaly detection models under our proposed fewshot anomaly detection setting, which shows GraphCore can increase average AUC by 5.8%, 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%, 22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 28 Jan 2023.

Builds on

19 of 29 references.

References · 19

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  5. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  6. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  7. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  8. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  9. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  10. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  11. 2022Vision GNN: An Image is Worth Graph of Nodescited by 185
  12. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
7 more
  1. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  2. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  3. 2020Explainable Deep One-Class Classificationcited by 126
  4. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  5. 2016Exploiting Cyclic Symmetry in Convolutional Neural Networkscited by 91
  6. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  7. 2019Fast Haar Transforms for Graph Neural Networkscited by 20

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 17; 2024: 19; 2025: 24; 2026: 15.

75 papers.

Sort:

By area:

  1. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  2. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  3. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  4. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  5. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  6. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  7. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  8. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  9. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
  10. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
  11. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  12. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
38 more
  1. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  2. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  3. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  4. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  5. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  6. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  7. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 28
  8. 2023An Incremental Unified Framework for Small Defect Inspectioncited by 25
  9. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  10. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  11. 2023GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detectioncited by 23
  12. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  13. 2023PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentationcited by 21
  14. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  15. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  16. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  17. 2025Handling Out-of-Distribution Data: A Surveycited by 14
  18. 2024COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detectioncited by 13
  19. 2024VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detectioncited by 13
  20. 2025MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learningcited by 12
  21. 2025Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detectioncited by 12
  22. 2025MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learningcited by 12
  23. 2023Optimizing PatchCore for Few/many-shot Anomaly Detectioncited by 10
  24. 2025Normal-Abnormal Guided Generalist Anomaly Detectioncited by 10
  25. 2026VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformercited by 10
  26. 2025DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookupcited by 9
  27. 2024Dual-path Frequency Discriminators for Few-shot Anomaly Detectioncited by 7
  28. 2025One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detectioncited by 7
  29. 2026SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modelingcited by 7
  30. 2023Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformerscited by 6
  31. 2024AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Modelcited by 6
  32. 2025FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detectioncited by 6
  33. 2025Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memorycited by 6
  34. 2023Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Visioncited by 5
  35. 2025Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shiftcited by 5
  36. 2025TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detectioncited by 5
  37. 2024Odd-One-Out: Anomaly Detection by Comparing with Neighborscited by 4
  38. 2025MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samplescited by 3

50 most cited of 75 All 75 (JSON)