Research Progress of Few-Shot Learning Methods Based on Graph Neural Networks

Few-shot learning (FSL) aims to learn to get a problem-solving model using a small number of samples. Under the trend of training models with big data, deep learning has gained success in many fields, but realistic scenarios often lack sufficient samples or labeled samples. Therefore, FSL becomes a promising research direction at present. Graph neural networks (GNN) have attracted great attention due to their excellent performance in many applications. In view of this, many methods try to use GNN for FSL. Currently there are few review researches related to FSL methods based on GNN, and there is a lack of division system and introductory work on this type of methods. We systematically compose the current work related to FSL based on GNN. The work outlines the basis and concepts of graph methods for FSL, broadly classifies them into four categories of methods based on node-based feature, edge-based feature, node-pair-based feature and class-level-based feature according to the basic ideas of the models. The research progress of the four methods is introduced as well. Then the experimental results of the commonly used few-shot datasets and representative models on these datasets are summarized, as well as the advantages and disadvantages of each type of methods. Finally, current status and challenges of the graph methods for FSL are introduced, and their future directions are prospected.