mlsanityData to 14 Oct 2026

Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval

arXiv 2308.06748 ↗First posted 13 Aug 2023cs.CVcited by 22

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Abstract (arXiv)

In this work, by re-examining the "matching" nature of Anomaly Detection (AD), we propose a new AD framework that simultaneously enjoys new records of AD accuracy and dramatically high running speed. In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion. Given a test sample, the top-K most similar training images are first selected based on a robust histogram matching process. Secondly, the nearest neighbor of each test patch is retrieved over the similar geometrical locations on those "global nearest neighbors", by using a carefully trained local metric. Finally, the anomaly score of each test image patch is calculated based on the distance to its "local nearest neighbor" and the "non-background" probability. The proposed method is termed "Cascade Patch Retrieval" (CPR) in this work. Different from the conventional patch-matching-based AD algorithms, CPR selects proper "targets" (reference images and locations) before "shooting" (patch-matching). On the well-acknowledged MVTec AD, BTAD and MVTec-3D AD datasets, the proposed algorithm consistently outperforms all the comparing SOTA methods by remarkable margins, measured by various AD metrics. Furthermore, CPR is extremely efficient. It runs at the speed of 113 FPS with the standard setting while its simplified version only requires less than 1 ms to process an image at the cost of a trivial accuracy drop. The code of CPR is available at https://github.com/flyinghu123/CPR.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 70 of its 105 references that are in the map. First posted on arXiv 13 Aug 2023.

Builds on

70 of 105 references.

References · 70

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38 more
  1. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  2. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  3. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
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  5. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  6. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
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  14. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
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Citing papers per year: 2024: 7; 2025: 10; 2026: 5.

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  1. 2025The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detectioncited by 30
  2. 2024Long-Tailed Anomaly Detection with Learnable Class Namescited by 14
  3. 2024AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarioscited by 9
  4. 2024Unlocking the Potential of Reverse Distillation for Anomaly Detectioncited by 8
  5. 2024Variational Autoencoder for Anomaly Detection: A Comparative Studycited by 6
  6. 2025Towards High-Resolution Industrial Image Anomaly Detectioncited by 3
  7. 2025MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samplescited by 3
  8. 2025From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challengecited by 2
  9. 2025ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learningcited by 2
  10. 2025Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detectioncited by 1
  11. 2025ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretrainingcited by 1
  12. 2026MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Modelscited by 1
10 more
  1. 2024Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Taskcited by 0
  2. 2024Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localizationcited by 0
  3. 2024Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tunerscited by 0
  4. 2025Anomalous Samples for Few-Shot Anomaly Detectioncited by 0
  5. 2025Self-Navigated Residual Mamba for Universal Industrial Anomaly Detectioncited by 0
  6. 2025Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localizationcited by 0
  7. 2026Deep Convolutional Large-Margin ℓp-SVDD for Visual Anomaly Detectioncited by 0
  8. 2026Understanding and Overcoming Cross-modal Fusion Bias in Multimodal Anomaly Detection From A Fisher Information Perspectivecited by 0
  9. 2026DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generationcited by 0
  10. 2026Focus and Supplement: Dual-Enhanced Vision Transformer for Multi-Class Anomaly Classificationcited by 0