  Learning from one or few visual examples is one of the key capabilities of
humans since early infancy, but is still a significant challenge for modern AI
systems. While considerable progress has been achieved in few-shot learning
from a few image examples, much less attention has been given to the verbal
descriptions that are usually provided to infants when they are presented with
a new object. In this paper, we focus on the role of additional semantics that
can significantly facilitate few-shot visual learning. Building upon recent
advances in few-shot learning with additional semantic information, we
demonstrate that further improvements are possible by combining multiple and
richer semantics (category labels, attributes, and natural language
descriptions). Using these ideas, we offer the community new results on the
popular miniImageNet and CUB few-shot benchmarks, comparing favorably to the
previous state-of-the-art results for both visual only and visual plus
semantics-based approaches. We also performed an ablation study investigating
the components and design choices of our approach.
