Abstract
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: this https URL.
Abstract (translated)
图像中的异常检测和定位是一个在计算机视觉中正在快速增长的研究领域。在这个领域，一个似乎被低估的问题是不显著异常聚类，即在完全无监督的情况下识别和分组不同类型的异常。在这项工作中，我们提出了一种在盲环境中对大型静止图像（纹理）进行异常聚类的新方法。也就是说，输入由正常和异常图像组成，没有区分和标签。导致任务困难的是，异常区域通常较小，可能仅出现轻微的视觉变化，这很容易被纹理的真正方差所掩盖。此外，每种异常类型可能具有复杂的形态分布。我们使用盲异常局部化和对比学习相结合的新方法来解决这个任务。通过高保真度地识别异常区域，我们可以将关注点限制在感兴趣的区域内；然后，对比学习被用于增加不同异常类型之间的分离度，并减少类内差异。我们的实验结果表明，与之前的工作相比，所提出的解决方案取得了显著更好的结果，设定了一个新的科技水平。项目页面：https:// this URL。
URL
https://arxiv.org/abs/2404.12246
