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Scene graphs are powerful representations that parse images into theirabstract semantic elements, i.e., objects and their interactions, whichfacilitates visual comprehension and explainable reasoning. On the other hand,commonsense knowledge graphs are rich repositories that encode how the world isstructured, and how general concepts interact. In this paper, we present aunified formulation of these two constructs, where a scene graph is seen as animage-conditioned instantiation of a commonsense knowledge graph. Based on thisnew perspective, we re-formulate scene graph generation as the inference of abridge between the scene and commonsense graphs, where each entity or predicateinstance in the scene graph has to be linked to its corresponding entity orpredicate class in the commonsense graph. To this end, we propose a novelgraph-based neural network that iteratively propagates information between thetwo graphs, as well as within each of them, while gradually refining theirbridge in each iteration. Our Graph Bridging Network, GB-Net, successivelyinfers edges and nodes, allowing to simultaneously exploit and refine the rich,heterogeneous structure of the interconnected scene and commonsense graphs.Through extensive experimentation, we showcase the superior accuracy of GB-Netcompared to the most recent methods, resulting in a new state of the art. Wepublicly release the source code of our method.

Bridging Knowledge Graphs to Generate Scene Graphs