Few-shot image classification with graph neural network (GNN) is a hot topic in recent years. Most GNN-based approaches have achieved promising performance. These methods utilize node features or one-dimensional edge feature for classification ignoring rich edge featues between nodes. In this letter, we propose a novel graph neural network exploiting multi-dimensional edge features (MDE-GNN) based on edge-labeling graph neural network (EGNN) and transductive neural network for few-shot learning. Unlike previous GNN-based approaches, we utilize multi-dimensional edge features information to construct edge matrices in graph. After layers of node and edge feautres updating, we generate a similarity score matrix by the mulit-dimensional edge features through a well-designed edge aggregation module. The parameters in our network are iteratively learnt by episode training with an edge similarity loss. We apply our model to supervised few-shot image classification tasks. Compared with previous GNNs and other few-shot learning approaches, we achieve state-of-the-art performance with two benchmark datasets.

中文翻译：

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少量图像分类的多维边缘特征图神经网络

使用图神经网络（GNN）进行少量图像分类是近年来的热门话题。大多数基于GNN的方法都取得了令人鼓舞的性能。这些方法利用节点特征或一维边缘特征进行分类，而忽略了节点之间的丰富边缘特征。在这封信中，我们提出了一种基于边标记图神经网络（EGNN）和转导神经网络的，利用多维边缘特征（MDE-GNN）的新颖图神经网络，可以进行多次学习。与以前的基于GNN的方法不同，我们利用多维边缘特征信息在图形中构造边缘矩阵。在节点和边缘特征层更新后，我们通过精心设计的边缘聚合模块，通过多维度边缘特征生成相似性得分矩阵。我们的网络中的参数是通过情节训练以边缘相似度损失进行迭代学习的。我们将模型应用于监督的少量照片分类任务。与以前的GNN和其他少量学习方法相比，我们使用两个基准数据集实现了最新的性能。