LERENet: Eliminating Intra-class Differences for Metal Surface Defect Few-shot Semantic Segmentation

arXiv:2403.11122v1 Announce Type: new
Abstract: Few-shot segmentation models excel in metal defect detection due to their rapid generalization ability to new classes and pixel-level segmentation, rendering them ideal for addressing data scarcity issues and achieving refined object delineation in industrial applications. Existing works neglect the \textit{Intra-Class Differences}, inherent in metal surface defect data, which hinders the model from learning sufficient knowledge from the support set to guide the query set segmentation. Specifically, it can be categorized into two types: the …

abstract applications arxiv class cs.cv data defect detection detection differences excel few-shot industrial metal object pixel rendering segmentation semantic surface them type