mlsanityData to 7 Oct 2026

High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis

arXiv 2304.06433 ↗First posted 13 Apr 2023cs.CVcited by 7

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Abstract (arXiv)

We propose a novel method for Zero-Shot Anomaly Localization on textures. The task refers to identifying abnormal regions in an otherwise homogeneous image. To obtain a high-fidelity localization, we leverage a bijective mapping derived from the 1-dimensional Wasserstein Distance. As opposed to using holistic distances between distributions, the proposed approach allows pinpointing the non-conformity of a pixel in a local context with increased precision. By aggregating the contribution of the pixel to the errors of all nearby patches we obtain a reliable anomaly score estimate. We validate our solution on several datasets and obtain more than a 40% reduction in error over the previous state of the art on the MVTec AD dataset in a zero-shot setting. Also see https://reality.tf.fau.de/pub/ardelean2024highfidelity.html.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 29 of its 64 references that are in the map. First posted on arXiv 13 Apr 2023.

Builds on

29 of 64 references.

References · 29

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  4. 2016Wide Residual Networkscited by 3,057
  5. 2013Describing Textures in the Wildcited by 2,110
  6. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  7. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  8. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
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  12. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
17 more
  1. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  2. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  3. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  4. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  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. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  8. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  9. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  10. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  11. 2020Deep Learning for Medical Anomaly Detection -- A Surveycited by 127
  12. 2020Deep Nearest Neighbor Anomaly Detectioncited by 103
  13. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  14. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  15. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  16. 2022MAEDAY: MAE for few and zero shot AnomalY-Detectioncited by 30
  17. 2022Generating natural images with direct Patch Distributions Matchingcited by 26

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2024Blind Localization and Clustering of Anomalies in Texturescited by 5
  2. 2024Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detectioncited by 4
  3. 2024Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Datacited by 2
  4. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  5. 2025Example-Based Feature Painting on Texturescited by 1
  6. 2025Quantized FCA: Efficient Zero-Shot Texture Anomaly Detectioncited by 0
  7. 2026Feature-Guided Diffusion for Non-Differentiable Inverse Renderingcited by 0