  Few-shot meta-learning has been recently reviving with expectations to mimic
humanity's fast adaption to new concepts based on prior knowledge. In this
short communication, we give a concise review on recent representative methods
in few-shot meta-learning, which are categorized into four branches according
to their technical characteristics. We conclude this review with some vital
current challenges and future prospects in few-shot meta-learning.
