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.

翻译：图像异常检测与定位是计算机视觉领域快速发展的研究方向。其中，异常聚类（即在完全无监督条件下识别并归类不同类型异常）是一个尚未得到充分研究的课题。本文提出一种针对准平稳图像（纹理）中异常目标进行盲聚类的新方法——输入数据包含无标注的正常图像与异常图像，且两类图像混合无序。该任务的难点在于：异常区域通常较小且外观变化细微，易被纹理本身的真实方差所掩盖；同时，每类异常可能呈现出复杂的外观分布。我们设计了一种结合盲异常定位与对比学习的创新方案：通过高保真度识别异常区域，将分析聚焦于感兴趣区域；进而利用对比学习增强不同异常类型间的可分性，同时降低类内差异。实验表明，所提方法相较于现有工作取得显著性能提升，达到了新的最优水平。项目页面：https://reality.tf.fau.de/pub/ardelean2024blind.html