  As a first step towards agents learning to communicate about their visual
environment, we propose a system that, given visual representations of a
referent (cat) and a context (sofa), identifies their discriminative
attributes, i.e., properties that distinguish them (has_tail). Moreover,
despite the lack of direct supervision at the attribute level, the model learns
to assign plausible attributes to objects (sofa-has_cushion). Finally, we
present a preliminary experiment confirming the referential success of the
predicted discriminative attributes.
