A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation

Abstract

The goal of \emph{generalized} few-shot semantic segmentation (GFSS) is to recognize \emph{novel-class} objects through training with a few annotated examples and the \emph{base-class} model that learned the knowledge about base classes. Unlike the \emph{classic} few-shot semantic segmentation, GFSS aims to classify pixels into both base and novel classes, meaning that GFSS is a more practical setting. To this end, the existing methods rely on such as customized models, carefully-designed loss functions, and transductive learning. However, we found that a simple rule and standard supervised learning substantially improve performances in GFSS. In this paper, we propose a simple yet effective method for GFSS without the aforementioned techniques employed in the existing methods. Moreover, we theoretically prove that our method perfectly maintains most of the base-class segmentation performances. Through numerical experiments, we demonstrate the effectiveness of the proposed method. In particular, our method improves the novel-class segmentation performances in the $1$-shot setting by $6.1\%$ on PASCAL-$5^i$ and $2.4\%$ on COCO-$20^i$.