  Scene-graph generation involves creating a structural representation of the
relationships between objects in a scene by predicting subject-object-relation
triplets from input data. However, existing methods show poor performance in
detecting triplets outside of a predefined set, primarily due to their reliance
on dependent feature learning. To address this issue we propose DDS -- a
decoupled dynamic scene-graph generation network -- that consists of two
independent branches that can disentangle extracted features. The key
innovation of the current paper is the decoupling of the features representing
the relationships from those of the objects, which enables the detection of
novel object-relationship combinations. The DDS model is evaluated on three
datasets and outperforms previous methods by a significant margin, especially
in detecting previously unseen triplets.
