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Papers/APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Seg...

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

Xuhai Chen, Yue Han, Jiangning Zhang

2023-05-27Anomaly DetectionNovelty DetectionAnomaly Classification
PaperPDFCode(official)Code

Abstract

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.

Results

TaskDatasetMetricValueModel
Anomaly DetectionMVTec ADDetection AUROC86.1APRIL-GAN（zero-shot）
Anomaly DetectionMVTec ADSegmentation AP40.8APRIL-GAN（zero-shot）
Anomaly DetectionMVTec ADSegmentation AUPRO44APRIL-GAN（zero-shot）
Anomaly DetectionMVTec ADSegmentation AUROC87.6APRIL-GAN（zero-shot）
Anomaly DetectionVisADetection AUROC78APRIL-GAN
Anomaly DetectionVisAF1-Score32.3APRIL-GAN
Anomaly DetectionVisASegmentation AUPRO86.8APRIL-GAN
Anomaly DetectionVisASegmentation AUROC94.2APRIL-GAN
Anomaly DetectionVisADetection AUROC78APRIL-GAN
2D ClassificationVisADetection AUROC78APRIL-GAN
Anomaly ClassificationVisADetection AUROC78APRIL-GAN

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