Papers › FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection
Donghyeong Kim, Chaewon Park, Suhwan Cho, Sangyoun Lee
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some methods do not meet the speed requirements of real-time inference, which is crucial for real-world applications. To address this issue, we propose a new method called Fast Adaptive Patch Memory (FAPM) for real-time industrial anomaly detection. FAPM utilizes patch-wise and layer-wise memory banks that store the embedding features of images at the patch and layer level, respectively, which eliminates unnecessary repetitive computations. We also propose patch-wise adaptive coreset sampling for faster and more accurate detection. FAPM performs well in both accuracy and speed compared to other state-of-the-art methods
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MVTec AD | FAPM | Detection AUROC | 99 | #47 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | FAPM | FPS | 44.1 | #47 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | FAPM | Segmentation AUROC | 98 | #47 of 148 | 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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