Multi-Branch Convolution Network for Few-Shot Classification

Abstract

Few-shot learning aims to complete the classification by only a small number of samples. In many few-shot learning frameworks, relation network is an end-to-end method, which can identify new categories through a small number of label samples based on metric learning. However, a simple feature extractor is used in this method, which limits the further improvement of the classification accuracy. To solve this problem, this paper proposes a multi-branch convolution network for feature extraction. This method combines the training strategies of multi-scale feature extraction, relation network, receptive field block and meta-learning. Firstly, the multi-scale feature vectors of the input image are extracted from the multi-branch convolution network. Then the feature vectors from the support set and the prediction set are input into the relation model, while the receptive field block is employed to improve the measurement ability of the network. Finally, the classification of the testing samples are realized according to the similarity score. In this paper, the effectiveness of the proposed model is verified on Omniglot and MiniImageNet datasets. The experimental results show that the classification accuracy of the proposed model is higher than that of other classical few-shot learning models.