探索大型视觉语言模型在鲁棒高效工业异常检测中的应用
Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection
AI总结：
针对工业异常检测问题，提出基于对比跨模态训练的视觉语言异常检测方法CLAD，利用大型视觉语言模型对齐多模态特征，在两个基准数据集上超越现有方法，兼具高性能与可解释性。
摘要：
工业异常检测（IAD）在制造过程的维护和质量控制中起着至关重要的作用。本文提出了一种新颖的方法——通过对比跨模态训练实现视觉语言异常检测（CLAD），该方法利用大型视觉语言模型（LVLMs）来改进工业场景中的异常检测和定位。CLAD通过对比学习将视觉和文本特征对齐到共享嵌入空间，确保正常实例被分组在一起，而异常实例被分开。通过在MVTec-AD和VisA两个工业基准数据集上进行广泛实验，我们证明CLAD在图像级异常检测和像素级异常定位方面均优于现有最先进方法。此外，我们提供了消融研究和人类评估，以验证方法中关键组件的重要性。我们的方法不仅实现了卓越的性能，还通过准确的异常定位增强了可解释性，使其成为现实工业应用的有前景解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
