mlsanityData to 14 Oct 2026

Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection

arXiv 2510.15602 ↗First posted 17 Oct 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Zero-shot anomaly localization is a rising field in computer vision research, with important progress in recent years. This work focuses on the problem of detecting and localizing anomalies in textures, where anomalies can be defined as the regions that deviate from the overall statistics, violating the stationarity assumption. The main limitation of existing methods is their high running time, making them impractical for deployment in real-world scenarios, such as assembly line monitoring. We propose a real-time method, named QFCA, which implements a quantized version of the feature correspondence analysis (FCA) algorithm. By carefully adapting the patch statistics comparison to work on histograms of quantized values, we obtain a 10x speedup with little to no loss in accuracy. Moreover, we introduce a feature preprocessing step based on principal component analysis, which enhances the contrast between normal and anomalous features, improving the detection precision on complex textures. Our method is thoroughly evaluated against prior art, comparing favorably with existing methods. Project page: https://reality.tf.fau.de/pub/ardelean2025quantized.html

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 58 references that are in the map. First posted on arXiv 17 Oct 2025.

Builds on

32 of 58 references.

References · 32

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2016Wide Residual Networkscited by 3,070
  3. 2013Describing Textures in the Wildcited by 2,125
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  5. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  6. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  7. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  8. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  9. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  10. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  11. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  12. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
20 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. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 130
  4. 2020Deep Learning for Medical Anomaly Detection -- A Surveycited by 127
  5. 2020Deep Nearest Neighbor Anomaly Detectioncited by 104
  6. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  7. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 68
  8. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  9. 2024ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentationcited by 49
  10. 2022MAEDAY: MAE for few and zero shot AnomalY-Detectioncited by 30
  11. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 29
  12. 2022Generating natural images with direct Patch Distributions Matchingcited by 27
  13. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 16
  14. 2023High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysiscited by 7
  15. 2024Few-Shot Anomaly Detection via Category-Agnostic Registration Learningcited by 6
  16. 2023Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representationscited by 5
  17. 2024Blind Localization and Clustering of Anomalies in Texturescited by 5
  18. 2024Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detectioncited by 4
  19. 2023That's BAD: Blind Anomaly Detection by Implicit Local Feature Clusteringcited by 3
  20. 2025Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detectioncited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025On the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection
  2. 2026SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling
  3. 2022DSR -- A dual subspace re-projection network for surface anomaly detection
  4. 2026VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer
  5. 2026Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion
  6. 2026TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection
  7. 2026AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors
  8. 2025Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation