In recent years, the focus on anomaly detection and localization in
industrial inspection tasks has intensified. While existing studies have
demonstrated impressive outcomes, they often rely heavily on extensive training
datasets or robust features extracted from pre-trained models trained on
diverse datasets like ImageNet. In this work, we propose a novel framework
leveraging the visual-linguistic CLIP model to adeptly train a backbone model
tailored to the manufacturing domain. Our approach concurrently considers
visual and text-aligned embedding spaces for normal and abnormal conditions.
The resulting pre-trained backbone markedly enhances performance in industrial
downstream tasks, particularly in anomaly detection and localization. Notably,
this improvement is substantiated through experiments conducted on multiple
datasets such as MVTecAD, BTAD, and KSDD2. Furthermore, using our pre-trained
backbone weights allows previous works to achieve superior performance in
few-shot scenarios with less training data. The proposed anomaly backbone
provides a foundation model for more precise anomaly detection and
localization.

近年来，对工业检测任务中的异常检测和定位的关注加强。本文利用视觉语言 CLIP 模型提出了一种新的框架来训练适用于制造领域的骨干模型，同时考虑了正常和异常条件的视觉和文本对齐嵌入空间。所得到的预训练骨干模型显著提高了工业下游任务的性能，尤其是在异常检测和定位方面。此外，使用我们的预训练骨干模型权重使先前的工作在少量训练数据的少样本情况下实现了更优异的性能。该提出的异常骨干模型为更精确的异常检测和定位提供了基础模型。