Deep Reasoning Network for Few-shot Semantic Segmentation

Abstract

Few-shot Semantic Segmentation (FSS) is a challenging problem in computer vision. It aims at segmenting objects of the unseen categories given only one or several annotated samples. The essence of FSS is to disseminate information from support images to query images for segmenting the mutual object categories. In this paper, we propose a Dynamic Reasoning Network (DRNet) to adaptively generate the parameters of predicting layers and infer the segmentation mask for each unseen category. More specifically, an Attentional Feature Integration Sub-network (AFIS) is first proposed to extract consistent features from support im-ages and query images. With shared weights, it stimulates the category consistency of different data streams. Then a Pooling-based Guidance Module (PGM) is used to cor-relate support features with query features progressively. To disseminate information from support images to various query images, we further propose a Dynamic PredictionModule (DPM) for generating the parameters of predicting layers. The proposed modules are unified for the dynamic reasoning of each query image segmentation. Experiments on two public benchmarks have demonstrated that our approach achieves superior performance and outperforms thevery recent state-of-the-art methods.