Papers › APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR...
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
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Classification | VisA | APRIL-GAN | Detection AUROC | 78.0 | #1 of 1 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | APRIL-GAN(zero-shot) | Detection AUROC | 86.1 | #120 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | APRIL-GAN(zero-shot) | Segmentation AP | 40.8 | #120 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | APRIL-GAN(zero-shot) | Segmentation AUPRO | 44.0 | #120 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | APRIL-GAN(zero-shot) | Segmentation AUROC | 87.6 | #120 of 148 | Archive leaderboard | report |
| Anomaly Detection | VisA | APRIL-GAN | Detection AUROC | 78.0 | #42 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | APRIL-GAN | F1-Score | 32.3 | #42 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | APRIL-GAN | Segmentation AUPRO | 86.8 | #42 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | APRIL-GAN | Segmentation AUROC | 94.2 | #42 of 50 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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