  Understanding visual relationships involves identifying the subject, the
object, and a predicate relating them. We leverage the strong correlations
between the predicate and the (subj,obj) pair (both semantically and spatially)
to predict the predicates conditioned on the subjects and the objects. Modeling
the three entities jointly more accurately reflects their relationships, but
complicates learning since the semantic space of visual relationships is huge
and the training data is limited, especially for the long-tail relationships
that have few instances. To overcome this, we use knowledge of linguistic
statistics to regularize visual model learning. We obtain linguistic knowledge
by mining from both training annotations (internal knowledge) and publicly
available text, e.g., Wikipedia (external knowledge), computing the conditional
probability distribution of a predicate given a (subj,obj) pair. Then, we
distill the knowledge into a deep model to achieve better generalization. Our
experimental results on the Visual Relationship Detection (VRD) and Visual
Genome datasets suggest that with this linguistic knowledge distillation, our
model outperforms the state-of-the-art methods significantly, especially when
predicting unseen relationships (e.g., recall improved from 8.45% to 19.17% on
VRD zero-shot testing set).
