Data to 7 Oct 2026

Sub-Image Anomaly Detection with Deep Pyramid Correspondences

arXiv 2005.02357 ↗First posted 5 May 2020cs.CV cs.LGcited by 297

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Abstract (arXiv)

Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image. In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid. SPADE is shown to achieve state-of-the-art performance on unsupervised anomaly detection and localization while requiring virtually no training time.

Key facts. Cited by 297 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 24; 2022: 45; 2023: 68; 2024: 74; 2025: 55; 2026: 31.

297 papers.

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2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
2023Deep Industrial Image Anomaly Detection: A Surveycited by 147

38 more

2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
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 128
2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 97
2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 47
2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 41
2023Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detectioncited by 39
2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
2023Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detectioncited by 32
2022ADTR: Anomaly Detection Transformer with Feature Reconstructioncited by 31
2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
2022MAEDAY: MAE for few and zero shot AnomalY-Detectioncited by 30

50 most cited of 297 All 297 (JSON)