Coarse-to-fine few-shot classification with deep metric learning

Abstract

Few-shot classification predicts the labels of unseen samples using only a few labeled samples, and employs the samples of the classes disjoint with unseen classes for model training. It faces two primary challenges, i.e., handling sample pairs with different similarity degrees by single classifier, and learning discriminant patterns from very few labeled samples per class. To address them, this work presents a Coarse-to-Fine few-shot classification framework under the guidance of Metric-based Auxiliary learning, abbreviated as CFMA. In particular, it discriminates the image pairs with large difference by capturing global features, and models the similarity relation of the image pairs with small difference by mining the local region of interests. Moreover, CFMA adopts deep metric learning to improve the model adaptivity on the set of limited samples, and generates pseudo labels to dynamically guide the coarse learning in iteration. Empirical studies on several benchmark databases, including mini-ImageNet, tiered-ImageNet, and CUB, demonstrate that our method achieves more promising classification performance compared to many state-of-the-art alternatives.