Structure-aware anomaly detection via semantic prototype reconstruction in hyperbolic space.

Anomaly detection plays a crucial role in various applications such as industrial defect inspection and safety perception. Existing mainstream approaches typically rely on modeling the distribution of normal samples to build anomaly discrimination models. However, these methods often face challenges in practical scenarios due to ambiguous decision boundaries and limited capability to capture complex semantic and structural variations in defects. To overcome these limitations, we propose a novel industrial defect detection framework based on hyperbolic space. This framework exploits the negative curvature property of hyperbolic geometry to dynamically extract semantic prototypes and embed them into the hyperbolic space, enhancing the model's ability to represent intricate semantic and structural changes. Furthermore, a semantic prototype-guided attention mechanism is integrated to assist the reconstruction of images, enabling accurate localization of anomalies through reconstruction error. Extensive experiments demonstrate that our method achieves state-of-the-art results on multiple industrial anomaly detection datasets. Notably, on the MVTec AD benchmark, our approach attains 99.8% and 99.1% AUROC scores for image-level and pixel-level tasks, respectively, significantly surpassing current leading methods.