A Graph Neural Network-based Few-shot Image Classification Method
To address the limitations of existing models in capturing comprehensive and diverse features for few-shot image classification tasks,a Graph-Based Dual-Encoding Prototype Network(GB-ProtoNet)is proposed,which employs a dual-feature extractor architecture that effectively captures and fuses global and local feature information by integrating two distinct neural networks.Specifically,a Residual Neural Network(ResNet)is combined with a graph neural network based on Graph SAmple and aggreGatE(GraphSAGE).The ResNet effectively propagates and preserves information across multiple network layers,enabling the extraction of global feature information.The GraphSAGE,on the other hand,shows good performance in processing graph-structured data by sampling and aggregating information from neighboring nodes to extract local feature information.During training,the GB-ProtoNet utilizes label-smoothing cross-entropy loss function to compute the losses and update the model parameters.Extensive experiments conducted on the miniImageNet and CUB-2002-2011 datasets demonstrate that the GB-ProtoNet achieves superior classification accuracy compared to other state-of-the-art models under both 5-Way 1-Shot and 5-Way 5-Shot settings.
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