  Inferring objects and their relationships from an image in the form of a
scene graph is useful in many applications at the intersection of vision and
language. We consider a challenging problem of compositional generalization
that emerges in this task due to a long tail data distribution. Current scene
graph generation models are trained on a tiny fraction of the distribution
corresponding to the most frequent compositions, e.g. <cup, on, table>.
However, test images might contain zero- and few-shot compositions of objects
and relationships, e.g. <cup, on, surfboard>. Despite each of the object
categories and the predicate (e.g. 'on') being frequent in the training data,
the models often fail to properly understand such unseen or rare compositions.
To improve generalization, it is natural to attempt increasing the diversity of
the training distribution. However, in the graph domain this is non-trivial. To
that end, we propose a method to synthesize rare yet plausible scene graphs by
perturbing real ones. We then propose and empirically study a model based on
conditional generative adversarial networks (GANs) that allows us to generate
visual features of perturbed scene graphs and learn from them in a joint
fashion. When evaluated on the Visual Genome dataset, our approach yields
marginal, but consistent improvements in zero- and few-shot metrics. We analyze
the limitations of our approach indicating promising directions for future
research.
