Anomaly detection (AD) has been extensively studied and applied in a wide
range of scenarios in the recent past. However, there are still gaps between
achieved and desirable levels of recognition accuracy for making AD for
practical applications. In this paper, we start from an insightful analysis of
two types of fundamental yet representative failure cases in the baseline
model, and reveal reasons that hinder current AD methods from achieving a
higher recognition accuracy. Specifically, by Case-1, we found that the main
reasons detrimental to current AD methods is that the inputs to the recovery
model contain a large number of detailed features to be recovered, which leads
to the normal/abnormal area has-not/has been recovered into its original state.
By Case-2, we surprisingly found that the abnormal area that cannot be
recognized in image-level representations can be easily recognized in the
feature-level representation. Based on the above observations, we propose a
novel Recover-then-Discriminate (ReDi) framework for AD. ReDi takes a
self-generated feature map and a selected prompted image as explicit input
information to solve problems in case-1. Concurrently, a feature-level
discriminative network is proposed to enhance abnormal differences between the
recovered representation and the input representation. Extensive experimental
results on two popular yet challenging AD datasets validate that ReDi achieves
the new state-of-the-art accuracy.

通过分析故障案例，揭示了当前异常检测方法未能实现更高识别准确性的原因，并提出了一种新的 Recover-then-Discriminate (ReDi) 框架，通过自动生成的特征图和选择的提示图像来解决问题，该框架在两个挑战性异常检测数据集上实现了最新的准确性。