  Images in the wild encapsulate rich knowledge about varied abstract concepts
and cannot be sufficiently described with models built only using image-caption
pairs containing selected objects. We propose to handle such a task with the
guidance of a knowledge base that incorporate many abstract concepts. Our
method is a two-step process where we first build a multi-entity-label image
recognition model to predict abstract concepts as image labels and then
leverage them in the second step as an external semantic attention and
constrained inference in the caption generation model for describing images
that depict unseen/novel objects. Evaluations show that our models outperform
most of the prior work for out-of-domain captioning on MSCOCO and are useful
for integration of knowledge and vision in general.

Computer Vision and Pattern Recognition;Computation and Language