Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
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 -shot setting by on PASCAL- and on COCO-.
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Kristjan Greenewald, Yuancheng Yu, et al.
NeurIPS 2024
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks