Abstract: Few shot learning is proposed to solve the problem of small size of data set required for model learning or high cost of data annotation in deep learning.Image classification has always been an important research content in the research field,and there may be insufficient annotation data.In view of the lack of image annotation data,researchers have put forward many solutions,one of which is to classify small sample images by using graph neural network.In order to better play the role of graph neural network in the field of small sample learning,aiming at the unstable situation of graph neural network convolution operation,residual graph convolution network is used to improve the graph neural network,and residual graph convolution network is designed to improve the stability of graph neural network.Based on the convolutional network of residual graph,the self-attention mechanism of residual graph is designed in combination with the self-attention mechanism,and the relationship between nodes is deeply mined to improve the efficiency of information transmission and improve the classification accuracy of the classification model.After testing,the training efficiency of the improved Res-GNN is improved.The classification accuracy in 5way-1shot task is 1.1% higher than that of GNN model,and 1.42% higher than that of GNN model in 5way-5shot task.In the 5way-1shot task,the classification accuracy of ResAT-GNN is 1.62% higher than that of GNN model.

Key words: Few shot learning, Image classification, Graph neural network, Residual network, Self-attention mechanism