  Relation prediction among entities in images is an important step in scene
graph generation (SGG), which further impacts various visual understanding and
reasoning tasks. Existing SGG frameworks, however, require heavy training yet
are incapable of modeling unseen (i.e.,zero-shot) triplets. In this work, we
stress that such incapability is due to the lack of commonsense reasoning,i.e.,
the ability to associate similar entities and infer similar relations based on
general understanding of the world. To fill this gap, we propose
CommOnsense-integrAted sCenegrapHrElation pRediction (COACHER), a framework to
integrate commonsense knowledge for SGG, especially for zero-shot relation
prediction. Specifically, we develop novel graph mining pipelines to model the
neighborhoods and paths around entities in an external commonsense knowledge
graph, and integrate them on top of state-of-the-art SGG frameworks. Extensive
quantitative evaluations and qualitative case studies on both original and
manipulated datasets from Visual Genome demonstrate the effectiveness of our
proposed approach.
