  Anomaly detection and localization in images is a growing field in computer
vision. In this area, a seemingly understudied problem is anomaly clustering,
i.e., identifying and grouping different types of anomalies in a fully
unsupervised manner. In this work, we propose a novel method for clustering
anomalies in largely stationary images (textures) in a blind setting. That is,
the input consists of normal and anomalous images without distinction and
without labels. What contributes to the difficulty of the task is that
anomalous regions are often small and may present only subtle changes in
appearance, which can be easily overshadowed by the genuine variance in the
texture. Moreover, each anomaly type may have a complex appearance
distribution. We introduce a novel scheme for solving this task using a
combination of blind anomaly localization and contrastive learning. By
identifying the anomalous regions with high fidelity, we can restrict our focus
to those regions of interest; then, contrastive learning is employed to
increase the separability of different anomaly types and reduce the intra-class
variation. Our experiments show that the proposed solution yields significantly
better results compared to prior work, setting a new state of the art. Project
page: https://reality.tf.fau.de/pub/ardelean2024blind.html.
