Multi-Scale End-To-End Learning For Point Cloud Geometry Compression

Anomaly localization is pixel-level regions detection in the image. The challenge is how to generate accurate representations of the novel anomaly types which are multifarious. Besides, the anomaly sample size is often not enough to support model learning to detection because of the limitations of real conditions. in this work, we present a novel few-shot setting for anomaly detection and reorganize the defective datasets. Based on a few-shot, we transfer the idea of metric learning and propose the prototype-guided transfer network (PGTNet). Extensive experiment results suggest that PGTNet outperforms current SOTA methods and provides a novel perspective for the anomaly localization task.