mlsanityData to 14 Oct 2026

CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization

arXiv 2206.04325 ↗First posted 9 Jun 2022cs.CV cs.LGcited by 135

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Abstract (arXiv)

For a long time, anomaly localization has been widely used in industries. Previous studies focused on approximating the distribution of normal features without adaptation to a target dataset. However, since anomaly localization should precisely discriminate normal and abnormal features, the absence of adaptation may make the normality of abnormal features overestimated. Thus, we propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes sophisticated anomaly localization using features adapted to the target dataset. CFA consists of (1) a learnable patch descriptor that learns and embeds target-oriented features and (2) scalable memory bank independent of the size of the target dataset. And, CFA adopts transfer learning to increase the normal feature density so that abnormal features can be clearly distinguished by applying patch descriptor and memory bank to a pre-trained CNN. The proposed method outperforms the previous methods quantitatively and qualitatively. For example, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in anomaly localization of MVTec AD benchmark. In addition, this paper points out the negative effects of biased features of pre-trained CNNs and emphasizes the importance of the adaptation to the target dataset. The code is publicly available at https://github.com/sungwool/CFA_for_anomaly_localization.

Key facts. Cited by 135 papers in the map (data through 14 Oct 2026). Builds on 19 of its 23 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

19 of 23 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  5. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  6. 2019On the Convergence of Adam and Beyondcited by 841
  7. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  8. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  9. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  10. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  11. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  12. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
7 more
  1. 2018An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solutioncited by 307
  2. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  3. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  4. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  5. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  6. 2021Fast and Accurate Model Scalingcited by 46
  7. 2021AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learningcited by 21

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 7; 2023: 26; 2024: 38; 2025: 38; 2026: 26.

135 papers.

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  1. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 171
  2. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  3. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 111
  4. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 93
  5. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  6. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  7. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  8. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  9. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  10. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 63
  11. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  12. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
38 more
  1. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  2. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  3. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  4. 2024Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generationcited by 32
  5. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  6. 2023ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detectioncited by 30
  7. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 26
  8. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  9. 2024SplatPose & Detect: Pose-Agnostic 3D Anomaly Detectioncited by 25
  10. 2024Tackling Structural Hallucination in Image Translation with Local Diffusioncited by 21
  11. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  12. 2022MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detectioncited by 19
  13. 2023Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detectioncited by 19
  14. 2025Learning to Detect Multi-class Anomalies with Just One Normal Image Promptcited by 18
  15. 2024Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detectioncited by 17
  16. 2022U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Thresholdcited by 16
  17. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  18. 2024PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edgecited by 16
  19. 2024Long-Tailed Anomaly Detection with Learnable Class Namescited by 14
  20. 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
  21. 2024AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localizationcited by 12
  22. 2022N-pad : Neighboring Pixel-based Industrial Anomaly Detectioncited by 11
  23. 2024Dynamic Addition of Noise in a Diffusion Model for Anomaly Detectioncited by 10
  24. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  25. 2023What makes a good data augmentation for few-shot unsupervised image anomaly detection?cited by 8
  26. 2023Invariant Anomaly Detection under Distribution Shifts: A Causal Perspectivecited by 8
  27. 2023High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysiscited by 7
  28. 2024A SAM-guided Two-stream Lightweight Model for Anomaly Detectioncited by 7
  29. 2024Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interferencecited by 7
  30. 2024A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detectioncited by 7
  31. 2024MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applicationscited by 7
  32. 2024SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentationcited by 7
  33. 2025Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detectioncited by 7
  34. 2023On Pixel-level Performance Assessment in Anomaly Detectioncited by 6
  35. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  36. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  37. 2025Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memorycited by 6
  38. 2023Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Visioncited by 5

50 most cited of 135 All 135 (JSON)