Template-guided Hierarchical Feature Restoration for Anomaly Detection
Targeting for detecting anomalies of various sizes for complicated normal patterns, we propose a Template-guided Hierarchical Feature Restoration method, which introduces two key techniques, bottleneck compression and template-guided compensation, for anomaly-free feature restoration. Specially, our framework compresses hierarchical features of an image by bottleneck structure to preserve the most crucial features shared among normal samples. We design template-guided compensation to restore the distorted features towards anomaly-free features. Particularly, we choose the most similar normal sample as the template and leverage hierarchical features from the template to compensate the distorted features. The bottleneck could partially filter out anomaly features, while the compensation further converts the reminding anomaly features towards normal with template guidance. Finally, anomalies are detected in terms of the cosine distance between the pre-trained features of an inference image and the corresponding restored anomaly-free features. Experimental results demonstrate the effectiveness of our approach, which achieves the state-of-the-art performance on the MVTec LOCO AD dataset.
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Datasets
MVTecAD
MVTec LOCO AD
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Anomaly Detection | MVTec AD | THFR | Detection AUROC | 99.2 | # 25 | |
| Segmentation AUROC | 98.2 | # 26 | ||||
| Segmentation AUPRO | 95.0 | # 16 | ||||
| Anomaly Detection | MVTec LOCO AD | THFR | Avg. Detection AUROC | 86.0 | # 11 | |
| Detection AUROC (only logical) | 85.2 | # 13 | ||||
| Detection AUROC (only structural) | 86.7 | # 17 | ||||
| Segmentation AU-sPRO (until FPR 5%) | 74.1 | # 3 |
