Contextual Affinity Distillation for Image Anomaly Detection

Previous studies on unsupervised industrial anomaly detection mainly focus on 'structural' types of anomalies such as cracks and color contamination by matching or learning local feature representations. While achieving significantly high detection performance on this kind of anomaly, they are faced with 'logical' types of anomalies that violate the long-range dependencies such as a normal object placed in the wrong position. Noting the reverse distillation approaches that are under the encoder-decoder paradigm could learn from the high abstract level knowledge, we propose to use two students (local and global) to better mimic the teacher's local and global behavior in reverse distillation. The local student, which is used in previous studies mainly focuses on accurate local feature learning while the global student pays attention to learning global correlations. 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 that the proposed method sets a new state-of-the-art performance on the MVTec LOCO AD dataset without using complex training techniques.