  We aim to bridge the gap between typical human and machine-learning
environments by extending the standard framework of few-shot learning to an
online, continual setting. In this setting, episodes do not have separate
training and testing phases, and instead models are evaluated online while
learning novel classes. As in the real world, where the presence of
spatiotemporal context helps us retrieve learned skills in the past, our online
few-shot learning setting also features an underlying context that changes
throughout time. Object classes are correlated within a context and inferring
the correct context can lead to better performance. Building upon this setting,
we propose a new few-shot learning dataset based on large scale indoor imagery
that mimics the visual experience of an agent wandering within a world.
Furthermore, we convert popular few-shot learning approaches into online
versions and we also propose a new contextual prototypical memory model that
can make use of spatiotemporal contextual information from the recent past.
