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.

翻译：我们提出了一种文本引导的变分图像生成方法，以解决工业制造中异常检测所需的清洁数据获取难题。该方法利用目标物体的文本信息——该信息从大量文本库文档中学习得到——生成与输入图像相似的无缺陷数据图像。所提出的框架确保生成的无缺陷图像与从文本和图像知识中推导出的预期分布保持一致，从而保障稳定性和通用性。实验结果表明，即使无缺陷数据有限，我们的方法仍能超越先前方法，展现了其有效性。通过四个基线模型和三个不同数据集的泛化测试，我们的方法得到了验证。我们还提供了补充分析，说明如何利用生成的图像提升异常检测模型的效果。