  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.
