Budget-aware Few-shot Learning via Graph Convolutional Network. (arXiv:2201.02304v1 [cs.CV])

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 …

arxiv budget cv few-shot learning graph learning network