PyramidCore – Feature Pyramids for Few-Shot Logical Anomaly Detection

Recent few-shot logical anomaly detection methods rely on external information for accurate detection. This is often done through handmade text prompts and category-specific procedures, making them infeasible to apply to new datasets. Full-shot methods do not utilise this additional information but extract meaningful representations of local and global structures. We hypothesise that a major drawback of few-shot logical anomaly detection methods is the over-reliance on external information and suboptimal image representation. However, matching the representations learned by full-shot methods is challenging due to the lack of data in a few-shot setting. We propose PyramidCore, a novel few-shot logical anomaly detection method that does not rely on external information but instead uses a robust appearance model that can be built from only a few examples. It builds a hierarchical model of object appearance, enabling the detection of complex logical anomalies at different scales. The proposed method achieves state-of-the-art results on the challenging MVTec LOCO Dataset.