Contextual Affinity Distillation for Image Anomaly Detection
Jie Zhang Masanori Suganuma Takayuki Okatani
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.