Wandering Within a World: Online Contextualized Few-Shot Learning

Understand

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.