  Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging
auxiliary knowledge, such as semantic representations. A limitation of previous
approaches is that only intrinsic properties of objects, e.g. their visual
appearance, are taken into account while their context, e.g. the surrounding
objects in the image, is ignored. Following the intuitive principle that
objects tend to be found in certain contexts but not others, we propose a new
and challenging approach, context-aware ZSL, that leverages semantic
representations in a new way to model the conditional likelihood of an object
to appear in a given context. Finally, through extensive experiments conducted
on Visual Genome, we show that contextual information can substantially improve
the standard ZSL approach and is robust to unbalanced classes.
