Summary of Text-guided Variational Image Generation For Industrial Anomaly Detection and Segmentation, by Mingyu Lee et al.
Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation
by Mingyu Lee, Jongwon Choi
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed text-guided variational image generation method addresses the challenge of obtaining clean data for anomaly detection in industrial manufacturing. The approach utilizes text information about target objects, learned from extensive documents, to generate non-defective images resembling input images. The framework ensures generated images align with anticipated distributions derived from textual and image-based knowledge, ensuring stability and generality. Experimental results demonstrate the effectiveness of the method, surpassing previous approaches even with limited non-defective data. The approach is validated through generalization tests across four baseline models and three distinct datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re trying to help machines learn to identify problems in factories by giving them more good examples to practice on. We use text information about objects to make fake images that are like the real thing, but not defective. This helps our machine learning models become better at spotting problems when they see new things. Our method is really good and works even with limited data. We tested it on four different models and three types of data. |
Keywords
» Artificial intelligence » Anomaly detection » Generalization » Image generation » Machine learning
