Weakly Supervised Few-Shot Semantic Segmentation via Pseudo Mask Enhancement and Meta Learning

Abstract

Few shot semantic segmentation has been proposed to enhance the generalization ability of traditional models with limited data. Previous works mainly focus on the supervised tasks, while limited amount of work is explored for the weakly supervised tasks. Weakly supervised semantic segmentation has become an active research area because weakly supervised labels effectively reduce the annotation cost of visual tasks. To this end, we propose a weakly supervised few-shot semantic segmentation model based on the meta learning framework, which utilizes prior knowledge and adjusts itself according to new tasks. Thereupon then, the proposed network is capable of both high efficiency and generalization ability to new tasks. In the pseudo mask generation stage, we develop a WRCAM method with the channel-spatial attention mechanism to refine the coverage size of targets in pseudo masks. In the few-shot semantic segmentation stage, the optimization based meta learning method is used to realize few-shot semantic segmentation by virtue of the refined pseudo masks. The experimental results show that the proposed method not only significantly outperforms weakly supervised SOTA methods, but also could be comparative to some supervised SOTA methods.