Improving Industrial Anomaly Clustering via Heatmap-Based Multi-Scale Views

Anomaly detection in industrial contexts has become a critical task in computer vision due to its high relevance to industry applications. By grouping anomalies into coherent clusters based on their characteristics, which is commonly known as anomaly clustering, a better understanding of the anomalies and their causes is possible. We propose a novel anomaly clustering framework that leverages anomaly localization to create multi-scale views for contrastive learning. Unlike previous approaches, it integrates heatmap-guided multi-view feature extraction with self-supervised clustering, improving robustness and scalability in industrial defect analysis. Our approach utilizes spatial localization of anomalies by a state-of-the-art anomaly detector to generate multiple views of the image at different scales. These views are then used to train a convolutional neural network by means of contrastive learning, allowing the network to learn effective data representations. This technique enhances the network’s ability to distinguish between the unique features of anomalies from different perspectives, improving representation learning for anomaly clustering. We demonstrate the effectiveness of our method on benchmark datasets, showing an improvement in NMI of 12.5% in clustering accuracy for objects and textures on the MVTec-AD dataset. Our approach sets a new state-of-the-art, advancing the capabilities of anomaly clustering in industrial applications.