mlsanityData to 7 Oct 2026

CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection

arXiv 2408.15628 ↗First posted 28 Aug 2024cs.CVcited by 11

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Abstract (arXiv)

To improve logical anomaly detection, some previous works have integrated segmentation techniques with conventional anomaly detection methods. Although these methods are effective, they frequently lead to unsatisfactory segmentation results and require manual annotations. To address these drawbacks, we develop an unsupervised component segmentation technique that leverages foundation models to autonomously generate training labels for a lightweight segmentation network without human labeling. Integrating this new segmentation technique with our proposed Patch Histogram module and the Local-Global Student-Teacher (LGST) module, we achieve a detection AUROC of 95.3% in the MVTec LOCO AD dataset, which surpasses previous SOTA methods. Furthermore, our proposed method provides lower latency and higher throughput than most existing approaches.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 28 Aug 2024.

Builds on

27 of 39 references.

References · 27

  1. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  2. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  3. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  4. 2016Wide Residual Networkscited by 3,057
  5. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  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. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  9. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  10. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  11. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  12. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
15 more
  1. 2023Segment Anything in High Qualitycited by 373
  2. 2023Recognize Anything: A Strong Image Tagging Modelcited by 305
  3. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  4. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  5. 2022Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labelscited by 218
  6. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  7. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  8. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  9. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  10. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  11. 2023Open-Set Image Tagging with Multi-Grained Text Supervisioncited by 67
  12. 2023ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detectioncited by 30
  13. 2023Component-aware anomaly detection framework for adjustable and logical industrial visual inspectioncited by 22
  14. 2023Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detectioncited by 19
  15. 2023Contextual Affinity Distillation for Image Anomaly Detectioncited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 5.

11 papers.

  1. 2024UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detectioncited by 28
  2. 2025LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detectioncited by 9
  3. 2025LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questionscited by 8
  4. 2025AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detectioncited by 8
  5. 2025SALAD -- Semantics-Aware Logical Anomaly Detectioncited by 3
  6. 2026UniSLAD: A Unified Framework for Structural and Logical Industrial Visual Anomaly Detectioncited by 1
  7. 2024LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Spacecited by 0
  8. 2026VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distractioncited by 0
  9. 2026BUSSARD: Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detectioncited by 0
  10. 2026LogiCo: A Unified Framework for Logical and Structural Anomaly Detectioncited by 0
  11. 2026Training-Free Logical and Structural Anomaly Detection via Calibrated Fusioncited by 0