Few-Shot Image Classification Algorithm of Graph Neural Network Based on Swin Transformer
In few-shot image classification tasks,capturing remote semantic information in feature extraction modules based on convolutional neural network and single measure of edge-feature similarity are challenging.Therefore,in this study,we present a few-shot image classification method utilizing a graph neural network based on Swin Transformer.First,the Swin Transformer is used to extract image features,which are utilized as node features in the graph neural network.Next,the edge-feature similarity measurement module is improved by adding additional metrics,thus forming a dual-measurement module to calculate the similarity between the node features.The obtained similarity is used as the edge-feature input of the graph neural network.Finally,the nodes and edges of the graph neural network are alternately updated to predict image class labels.The classification accuracy of our proposed method for a 5-way 1-shot task on Stanford Dogs,Stanford Cars,and CUB-200-2011 datasets is calculated as 85.21%,91.10%,and 91.08%,respectively,thereby achieving significant results in few-shot image classification.