  Few-shot Learning (FSL) aims to classify new concepts from a small number of
examples. While there have been an increasing amount of work on few-shot object
classification in the last few years, most current approaches are limited to
images with only one centered object. On the opposite, humans are able to
leverage prior knowledge to quickly learn new concepts, such as semantic
relations with contextual elements. Inspired by the concept of contextual
learning in educational sciences, we propose to make a step towards adopting
this principle in FSL by studying the contribution that context can have in
object classification in a low-data regime. To this end, we first propose an
approach to perform FSL on images of complex scenes. We develop two
plug-and-play modules that can be incorporated into existing FSL methods to
enable them to leverage contextual learning. More specifically, these modules
are trained to weight the most important context elements while learning a
particular concept, and then use this knowledge to ground visual class
representations in context semantics. Extensive experiments on Visual Genome
and Open Images show the superiority of contextual learning over learning
individual objects in isolation.
