  The scene graph generation (SGG) task involves detecting objects within an
image and predicting predicates that represent the relationships between the
objects. However, in SGG benchmark datasets, each subject-object pair is
annotated with a single predicate even though a single predicate may exhibit
diverse semantics (i.e., semantic diversity), existing SGG models are trained
to predict the one and only predicate for each pair. This in turn results in
the SGG models to overlook the semantic diversity that may exist in a
predicate, thus leading to biased predictions. In this paper, we propose a
novel model-agnostic Semantic Diversity-aware Prototype-based Learning (DPL)
framework that enables unbiased predictions based on the understanding of the
semantic diversity of predicates. Specifically, DPL learns the regions in the
semantic space covered by each predicate to distinguish among the various
different semantics that a single predicate can represent. Extensive
experiments demonstrate that our proposed model-agnostic DPL framework brings
significant performance improvement on existing SGG models, and also
effectively understands the semantic diversity of predicates.
