Few-shot image classification using graph neural network with fine-grained feature descriptors

Abstract

Graph computation via Graph Neural Networks (GNNs) is emerging as a pivotal approach for addressing the challenges in image classification tasks. This paper introduces a novel strategy for image classification using minimal labeled data from the mini-ImageNet database. The primary contributions include the development of an innovative Fine-Grained Feature Descriptor (FGFD) module. Following this, the GNN is employed at a more granular level to enhance image classification efficiency. Additionally, ablation studies were conducted in conjunction with existing state-of-the-art systems for few-shot image classification. Comparative analyses were performed, and the simulation results demonstrate that the proposed method significantly improves classification accuracy over traditional few-shot image classification methods.