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。