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

TL;DR

This paper introduces a text-guided variational image generation technique to produce non-defective images for industrial anomaly detection, improving detection accuracy with limited data.

Contribution

It presents a novel framework that leverages text information to generate realistic non-defective images, enhancing anomaly detection in industrial settings.

Findings

Outperforms previous methods with limited data

Effective across multiple datasets and models

Improves anomaly detection accuracy

Abstract

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.