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In this technical report, we briefly introduce our solution for theZero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023Challenge. For industrial visual inspection, building a single model that canbe rapidly adapted to numerous categories without or with only a few normalreference images is a promising research direction. This is primarily becauseof the vast variety of the product types. For the zero-shot track, we propose asolution based on the CLIP model by adding extra linear layers. These layersare used to map the image features to the joint embedding space, so that theycan compare with the text features to generate the anomaly maps. Besides, whenthe reference images are available, we utilize multiple memory banks to storetheir features and compare them with the features of the test images during thetesting phase. In this challenge, our method achieved first place in thezero-shot track, especially excelling in segmentation with an impressive F1score improvement of 0.0489 over the second-ranked participant. Furthermore, inthe few-shot track, we secured the fourth position overall, with ourclassification F1 score of 0.8687 ranking first among all participating teams.

APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation  Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on  Zero-shot AD and 4th Place on Few-shot AD