mlsanityData to 14 Oct 2026

MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities

arXiv 2205.00908 ↗First posted 2 May 2022cs.CVcited by 68

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Abstract (arXiv)

Under the semi-supervised framework, we propose an end-to-end memory-based segmentation network (MemSeg) to detect surface defects on industrial products. Considering the small intra-class variance of products in the same production line, from the perspective of differences and commonalities, MemSeg introduces artificially simulated abnormal samples and memory samples to assist the learning of the network. In the training phase, MemSeg explicitly learns the potential differences between normal and simulated abnormal images to obtain a robust classification hyperplane. At the same time, inspired by the mechanism of human memory, MemSeg uses a memory pool to store the general patterns of normal samples. By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end manner. Through experimental validation, MemSeg achieves the state-of-the-art (SOTA) performance on MVTec AD datasets with AUC scores of 99.56% and 98.84% at the image-level and pixel-level, respectively. In addition, MemSeg also has a significant advantage in inference speed benefiting from the end-to-end and straightforward network structure, which better meets the real-time requirement in industrial scenarios.

Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 2 May 2022.

Builds on

20 of 31 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  4. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  5. 2013Describing Textures in the Wildcited by 2,125
  6. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  7. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  8. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  9. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  10. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  11. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  12. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
8 more
  1. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  2. 2021Coordinate Attention for Efficient Mobile Network Designcited by 241
  3. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
  4. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  5. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  6. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  7. 2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
  8. 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: 3; 2023: 15; 2024: 30; 2025: 13; 2026: 7.

68 papers.

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  1. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  2. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  3. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  4. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  5. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  6. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  7. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  8. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
  9. 2024ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentationcited by 49
  10. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  11. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  12. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
38 more
  1. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43
  2. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 42
  3. 2024GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detectioncited by 42
  4. 2024Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Promptscited by 40
  5. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  6. 2023ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detectioncited by 30
  7. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  8. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  9. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  10. 2024Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detectioncited by 17
  11. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 16
  12. 2024Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentationcited by 11
  13. 2023SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classificationcited by 10
  14. 2024DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learningcited by 10
  15. 2024Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detectioncited by 9
  16. 2024Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detectioncited by 8
  17. 2024A SAM-guided Two-stream Lightweight Model for Anomaly Detectioncited by 7
  18. 2024ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detectioncited by 7
  19. 2025A Survey on Industrial Anomaly Synthesiscited by 7
  20. 2025Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detectioncited by 7
  21. 2023Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformerscited by 6
  22. 2024A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptationcited by 6
  23. 2024RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observationscited by 5
  24. 2025SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmarkcited by 5
  25. 2025Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectorscited by 5
  26. 2024Test Time Training for Industrial Anomaly Segmentationcited by 4
  27. 2025Deep Learning in Automated Power Line Inspection: A Reviewcited by 4
  28. 2025Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detectioncited by 4
  29. 2025SALAD -- Semantics-Aware Logical Anomaly Detectioncited by 4
  30. 2026AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectorscited by 4
  31. 2022Self-Supervised Training with Autoencoders for Visual Anomaly Detectioncited by 3
  32. 2023Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Datasetcited by 3
  33. 2024Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flowscited by 3
  34. 2024MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detectioncited by 3
  35. 2024ONER: Online Experience Replay for Incremental Anomaly Detectioncited by 3
  36. 2024ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinementcited by 2
  37. 2023Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Datacited by 1
  38. 2024AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detectioncited by 1

50 most cited of 68 All 68 (JSON)