Previous works on unsupervised industrial anomaly detection mainly focus on
local structural anomalies such as cracks and color contamination. While
achieving significantly high detection performance on this kind of anomaly,
they are faced with logical anomalies that violate the long-range dependencies
such as a normal object placed in the wrong position. In this paper, based on
previous knowledge distillation works, we propose to use two students (local
and global) to better mimic the teacher's behavior. The local student, which is
used in previous studies mainly focuses on structural anomaly detection while
the global student pays attention to logical anomalies. To further encourage
the global student's learning to capture long-range dependencies, we design the
global context condensing block (GCCB) and propose a contextual affinity loss
for the student training and anomaly scoring. Experimental results show the
proposed method doesn't need cumbersome training techniques and achieves a new
state-of-the-art performance on the MVTec LOCO AD dataset.

基于先前知识蒸馏方法，本文提出使用两个学生（局部和全局）更好地模仿教师的行为，局部学生主要关注结构异常检测，而全局学生则关注逻辑异常，并通过设计全局上下文压缩块（GCCB）和提出上下文相似度损失来进一步鼓励全局学生学习以捕捉长距离依赖关系，实验结果表明该方法在 MVTec LOCO AD 数据集上达到了最新的性能水平，且无需繁琐的训练技巧。