  In this technical report, we briefly introduce our solution for the
Zero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023
Challenge. For industrial visual inspection, building a single model that can
be rapidly adapted to numerous categories without or with only a few normal
reference images is a promising research direction. This is primarily because
of the vast variety of the product types. For the zero-shot track, we propose a
solution based on the CLIP model by adding extra linear layers. These layers
are used to map the image features to the joint embedding space, so that they
can compare with the text features to generate the anomaly maps. Besides, when
the reference images are available, we utilize multiple memory banks to store
their features and compare them with the features of the test images during the
testing phase. In this challenge, our method achieved first place in the
zero-shot track, especially excelling in segmentation with an impressive F1
score improvement of 0.0489 over the second-ranked participant. Furthermore, in
the few-shot track, we secured the fourth position overall, with our
classification F1 score of 0.8687 ranking first among all participating teams.
