mlsanityData to 7 Oct 2026

Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation

arXiv 2403.06247 ↗First posted 10 Mar 2024cs.CV cs.AIcited by 11

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Abstract (arXiv)

We propose a text-guided variational image generation method to address the challenge of getting clean data for anomaly detection in industrial manufacturing. Our method utilizes text information about the target object, learned from extensive text library documents, to generate non-defective data images resembling the input image. The proposed framework ensures that the generated non-defective images align with anticipated distributions derived from textual and image-based knowledge, ensuring stability and generality. Experimental results demonstrate the effectiveness of our approach, surpassing previous methods even with limited non-defective data. Our approach is validated through generalization tests across four baseline models and three distinct datasets. We present an additional analysis to enhance the effectiveness of anomaly detection models by utilizing the generated images.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 10 Mar 2024.

Builds on

21 of 30 references.

References · 21

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  3. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  4. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  6. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  7. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  8. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  9. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  10. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  11. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  12. 2022VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidancecited by 285
9 more
  1. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  2. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  3. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  4. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  5. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  6. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  7. 2022CLIP-Event: Connecting Text and Images with Event Structurescited by 86
  8. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  9. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60

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Cited by year

Citing papers per year: 2024: 1; 2025: 10.

11 papers.

  1. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  2. 2025Normal-Abnormal Guided Generalist Anomaly Detectioncited by 10
  3. 2025PanoGen++: Domain-Adapted Text-Guided Panoramic Environment Generation for Vision-and-Language Navigationcited by 6
  4. 2025CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filteringcited by 5
  5. 2025Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detectioncited by 4
  6. 2025VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Modelscited by 3
  7. 2025PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awarenesscited by 2
  8. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  9. 2024AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localizationcited by 1
  10. 2025MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detectioncited by 1
  11. 2025Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generationcited by 0