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Scene graph generation (SGG) is built on top of detected objects to predictobject pairwise visual relations for describing the image content abstraction.Existing works have revealed that if the links between objects are given asprior knowledge, the performance of SGG is significantly improved. Inspired bythis observation, in this article, we propose a relation regularized network(R2-Net), which can predict whether there is a relationship between two objectsand encode this relation into object feature refinement and better SGG.Specifically, we first construct an affinity matrix among detected objects torepresent the probability of a relationship between two objects. Graphconvolution networks (GCNs) over this relation affinity matrix are then used asobject encoders, producing relation-regularized representations of objects.With these relation-regularized features, our R2-Net can effectively refineobject labels and generate scene graphs. Extensive experiments are conducted onthe visual genome dataset for three SGG tasks (i.e., predicate classification,scene graph classification, and scene graph detection), demonstrating theeffectiveness of our proposed method. Ablation studies also verify the keyroles of our proposed components in performance improvement.

Relation Regularized Scene Graph Generation