Multi-granularity Recurrent Attention Graph Neural Network for Few-Shot Learning

Few-shot learning aims to learn a classifier that classifies unseen classes well with limited labeled samples. Existing meta learning-based works, whether graph neural network or other baseline approaches in few-shot learning, has benefited from the meta-learning process with episodic tasks to enhance the generalization ability. However, the performance of meta-learning is greatly affected by the initial embedding network, due to the limited number of samples. In this paper, we propose a novel Multi-granularity Recurrent Attention Graph Neural Network (MRA-GNN), which employs Multi-granularity graph to achieve better generalization ability for few-shot learning. We first construct the Local Proposal Network (LPN) based on attention to generate local images from foreground images. The intra-cluster similarity and the inter-cluster dissimilarity are considered in the local images to generate discriminative features. Finally, we take the local images and original images as the input of multi-grained GNN models to perform classification. We evaluate our work by extensive comparisons with previous GNN approaches and other baseline methods on two benchmark datasets (i.e., miniImageNet and CUB). The experimental study on both of the supervised and semi-supervised few-shot image classification tasks demonstrates the proposed MRA-GNN significantly improves the performances and achieves the state-of-the-art results we know.