Few-Shot Semantic Segmentation via Frequency Guided Neural Network
Xiya Rao, Tao Lü, Zhongyuan Wang, Yanduo Zhang · IEEE Signal Processing Letters · 2022
Prototype learning is extensively used in few-shot semantic segmentation due to its excellent capability of semantic information extraction and effective prevention of overfitting. The previous prototype based methods ignore the frequency discrepancy inside the object, thereby leading to semantic confusion of the object. In this paper, we propose a frequency guided network (FGNet) which explicitly models the semantic information of different frequencies and precisely guides the semantic alignment of the object. Specifically, the proposed FGNet consists of two modules: a frequency separation module (FSM) and a multi-guided feature enrichment module (MG-FEM) to complete the multi-frequency semantic information extraction and alignment, respectively. Experiments on PASCAL-$5^{i}$dataset show that our FGNet achieves mIoU score of 61.2% in 1-shot which surpasses the state-of-the-art methods.