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Scene-graph generation involves creating a structural representation of therelationships between objects in a scene by predicting subject-object-relationtriplets from input data. However, existing methods show poor performance indetecting triplets outside of a predefined set, primarily due to their relianceon dependent feature learning. To address this issue we propose DDS -- adecoupled dynamic scene-graph generation network -- that consists of twoindependent branches that can disentangle extracted features. The keyinnovation of the current paper is the decoupling of the features representingthe relationships from those of the objects, which enables the detection ofnovel object-relationship combinations. The DDS model is evaluated on threedatasets and outperforms previous methods by a significant margin, especiallyin detecting previously unseen triplets.

DDS: Decoupled Dynamic Scene-Graph Generation Network