Multi-scale feature network for few-shot learning

Few-shot learning aims to learn a classifier that has good generalization performance in new classes, where each class only a small number of labeled examples are available. The existing few-shot classification methods use the single-scale image do not learn effective feature representation. Moreover, most of previous methods still depend on standard metrics to calculate visual similarities, such as Euclidean or cosine distance. Standard metrics are independent of data and lack nonlinear internal structure that captures the similarity between data. In this paper, we propose a new method for few-shot learning problem, which learns a multi-scale feature space, and classification is performed by computing similarities between the multi-scale representation of the image and the label feature of each class (i.e. class representation). Our method, called the Multi-Scale Feature Network (MSFN), is trained end-to-end from scratch. The proposed method improves 1-shot accuracy from 50.44% to 54.48% and 5-shot accuracy from 68.2% to 69.06% on MiniImagenet dataset compared to competing approaches. Experimental results on Omniglot, MiniImagenet, Cifar100, CUB200, and Caltech256 datasets demonstrate the effectiveness of the proposed method.