Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning

Abstract

Few-shot learning refers to training a model with a few labeled data to effectively recognize unseen categories. Recently, numerous approaches have been suggested to improve the extraction of abundant feature information at hierarchical layers or multiple scales for similarity metrics, especially methods based on learnable relation networks, which have demonstrated promising results. However, the roles played by image features in relationship measurement vary at different layers, and effectively integrating features from different layers and multiple scales can improve the measurement capacity of the model. In light of this, we propose a novel method called dual-branch multi-scale relation networks with tutoring learning (DbMRNT) for few-shot learning. Specifically, we first generate deep multiple features using a multi-scale feature generator in Branch 1 while extracting features at hierarchical layers in Branch 2. Then, learnable relation networks are employed in both branches to measure the pairwise similarity of features at each scale or layer. Furthermore, to leverage the dominant role of deep features in the final classification, we introduce a tutorial learning module that enables Branch 1 to tutor the learning process of Branch 2. Ultimately, the relation scores of all scales and layers are integrated to obtain the classification results. Extensive experiments on popular few-shot learning datasets prove that our method outperforms other similar methods.