mlsanityData to 14 Oct 2026

LogicAL: Towards logical anomaly synthesis for unsupervised anomaly localization

arXiv 2405.06875 ↗First posted 11 May 2024cs.CVcited by 7

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Abstract (arXiv)

Anomaly localization is a practical technology for improving industrial production line efficiency. Due to anomalies are manifold and hard to be collected, existing unsupervised researches are usually equipped with anomaly synthesis methods. However, most of them are biased towards structural defects synthesis while ignoring the underlying logical constraints. To fill the gap and boost anomaly localization performance, we propose an edge manipulation based anomaly synthesis framework, named LogicAL, that produces photo-realistic both logical and structural anomalies. We introduce a logical anomaly generation strategy that is adept at breaking logical constraints and a structural anomaly generation strategy that complements to the structural defects synthesis. We further improve the anomaly localization performance by introducing edge reconstruction into the network structure. Extensive experiments on the challenge MVTecLOCO, MVTecAD, VisA and MADsim datasets verify the advantage of proposed LogicAL on both logical and structural anomaly localization.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 27 of its 36 references that are in the map. First posted on arXiv 11 May 2024.

Builds on

27 of 36 references.

References · 27

  1. 2023Segment Anythingcited by 8,234
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15 more
  1. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  2. 2021Pixel Difference Networks for Efficient Edge Detectioncited by 168
  3. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  4. 2016Richer Convolutional Features for Edge Detectioncited by 143
  5. 2019Detecting the Unexpected via Image Resynthesiscited by 137
  6. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  7. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  8. 2020Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentationcited by 96
  9. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
  10. 2020A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Videocited by 86
  11. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  12. 2020G2D: Generate to Detect Anomalycited by 42
  13. 2020Image Shape Manipulation from a Single Augmented Training Samplecited by 13
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  15. 2023SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classificationcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2025Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detectioncited by 5
  2. 2024AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localizationcited by 1
  3. 2025AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detectioncited by 1
  4. 2025TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bankcited by 0
  5. 2026When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detectioncited by 0
  6. 2026LogiCo: A Unified Framework for Logical and Structural Anomaly Detectioncited by 0
  7. 2026Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomaliescited by 0