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

我们提出了一种新的方法来在纹理图像中盲目聚类异常，通过高精度地识别异常区域并使用对比学习增加不同异常类型的可分离性和降低类内变异，我们的实验表明所提出的解决方案相比以往工作有了显著的改进，树立了新的技术水平。

纹理的盲定位和异常聚类

Blind Localization and Clustering of Anomalies in Textures

The core idea of visual anomaly detection is to learn the normality from
normal images, but previous works have been developed specifically for certain
tasks, leading to fragmentation among various tasks: defect detection, semantic
anomaly detection, multi-class anomaly detection, and anomaly clustering. This
one-task-one-model approach is resource-intensive and incurs high maintenance
costs as the number of tasks increases. This paper presents SelFormaly, a
universal and powerful anomaly detection framework. We emphasize the necessity
of our off-the-shelf approach by pointing out a suboptimal issue with
fluctuating performance in previous online encoder-based methods. In addition,
we question the effectiveness of using ConvNets as previously employed in the
literature and confirm that self-supervised ViTs are suitable for unified
anomaly detection. We introduce back-patch masking and discover the new role of
top k-ratio feature matching to achieve unified and powerful anomaly detection.
Back-patch masking eliminates irrelevant regions that possibly hinder
target-centric detection with representations of the scene layout. The top
k-ratio feature matching unifies various anomaly levels and tasks. Finally,
SelFormaly achieves state-of-the-art results across various datasets for all
the aforementioned tasks.

SelFormaly 是一个通用且强大的异常检测框架，通过引入 back-patch masking 和 top k-ratio feature matching，它实现了统一且强大的异常检测，同时在各种数据集上取得了最先进的结果。