  Few-shot image classification is a challenging problem that aims to achieve
the human level of recognition based only on a small number of training images.
One main solution to few-shot image classification is deep metric learning.
These methods, by classifying unseen samples according to their distances to
few seen samples in an embedding space learned by powerful deep neural
networks, can avoid overfitting to few training images in few-shot image
classification and have achieved the state-of-the-art performance. In this
paper, we provide an up-to-date review of deep metric learning methods for
few-shot image classification from 2018 to 2022 and categorize them into three
groups according to three stages of metric learning, namely learning feature
embeddings, learning class representations, and learning distance measures.
With this taxonomy, we identify the novelties of different methods and problems
they face. We conclude this review with a discussion on current challenges and
future trends in few-shot image classification.
