  We present a novel bi-modal system based on deep networks to address the
problem of learning associations and simple meanings of objects depicted in
"authored" images, such as fine art paintings and drawings. Our overall system
processes both the images and associated texts in order to learn associations
between images of individual objects, their identities and the abstract
meanings they signify. Unlike past deep nets that describe depicted objects and
infer predicates, our system identifies meaning-bearing objects ("signifiers")
and their associations ("signifieds") as well as basic overall meanings for
target artworks. Our system had precision of 48% and recall of 78% with an F1
metric of 0.6 on a curated set of Dutch vanitas paintings, a genre celebrated
for its concentration on conveying a meaning of great import at the time of
their execution. We developed and tested our system on fine art paintings but
our general methods can be applied to other authored images.
