Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly
  Detection
Jianjian Qin Chunzhi Gu Jun Yu Chao Zhang
Anomaly detection (AD) is a fundamental task in computer vision. It aims to
identify incorrect image data patterns which deviate from the normal ones.
Conventional methods generally address AD by preparing augmented negative
samples to enforce self-supervised learning. However, these techniques
typically do not consider semantics during augmentation, leading to the
generation of unrealistic or invalid negative samples. Consequently, the
feature extraction network can be hindered from embedding critical features. In
this study, inspired by visual attention learning approaches, we propose
CutSwap, which leverages saliency guidance to incorporate semantic cues for
augmentation. Specifically, we first employ LayerCAM to extract multilevel
image features as saliency maps and then perform clustering to obtain multiple
centroids. To fully exploit saliency guidance, on each map, we select a pixel
pair from the cluster with the highest centroid saliency to form a patch pair.
Such a patch pair includes highly similar context information with dense
semantic correlations. The resulting negative sample is created by swapping the
locations of the patch pair. Compared to prior augmentation methods, CutSwap
generates more subtle yet realistic negative samples to facilitate quality
feature learning. Extensive experimental and ablative evaluations demonstrate
that our method achieves state-of-the-art AD performance on two mainstream AD
benchmark datasets.