Industrial anomaly detection (IAD) plays a crucial role in the maintenance and quality control of manufacturing processes. In this paper, we propose a novel approach, Vision-Language Anomaly Detection via Contrastive Cross-Modal Training (CLAD), which leverages large vision-language models (LVLMs) to improve both anomaly detection and localization in industrial settings. CLAD aligns visual and textual features into a shared embedding space using contrastive learning, ensuring that normal instances are grouped together while anomalies are pushed apart. Through extensive experiments on two benchmark industrial datasets, MVTec-AD and VisA, we demonstrate that CLAD outperforms state-of-the-art methods in both image-level anomaly detection and pixel-level anomaly localization. Additionally, we provide ablation studies and human evaluation to validate the importance of key components in our method. Our approach not only achieves superior performance but also enhances interpretability by accurately localizing anomalies, making it a promising solution for real-world industrial applications.

本研究针对工业异常检测（IAD）中存在的挑战，提出了一种新颖的方法，即通过对比跨模态训练的视觉-语言异常检测（CLAD）。该方法利用大型视觉语言模型改善异常检测和定位的性能，并通过实验证明在图像级和像素级异常检测方面优于现有方法，具有重要的现实应用潜力。

探索大型视觉语言模型用于稳健高效的工业异常检测