  This paper tackles the problem of few-shot learning, which aims to learn new
visual concepts from a few examples. A common problem setting in few-shot
classification assumes random sampling strategy in acquiring data labels, which
is inefficient in practical applications. In this work, we introduce a new
budget-aware few-shot learning problem that not only aims to learn novel object
categories, but also needs to select informative examples to annotate in order
to achieve data efficiency.
  We develop a meta-learning strategy for our budget-aware few-shot learning
task, which jointly learns a novel data selection policy based on a Graph
Convolutional Network (GCN) and an example-based few-shot classifier. Our
selection policy computes a context-sensitive representation for each unlabeled
data by graph message passing, which is then used to predict an informativeness
score for sequential selection. We validate our method by extensive experiments
on the mini-ImageNet, tiered-ImageNet and Omniglot datasets. The results show
our few-shot learning strategy outperforms baselines by a sizable margin, which
demonstrates the efficacy of our method.
