Relational Prior Knowledge Graphs for Detection and Instance
  Segmentation
Osman Ülger Yu Wang Ysbrand Galama Sezer Karaoglu Theo Gevers Martin R. Oswald
Humans have a remarkable ability to perceive and reason about the world
around them by understanding the relationships between objects. In this paper,
we investigate the effectiveness of using such relationships for object
detection and instance segmentation. To this end, we propose a Relational
Prior-based Feature Enhancement Model (RP-FEM), a graph transformer that
enhances object proposal features using relational priors. The proposed
architecture operates on top of scene graphs obtained from initial proposals
and aims to concurrently learn relational context modeling for object detection
and instance segmentation. Experimental evaluations on COCO show that the
utilization of scene graphs, augmented with relational priors, offer benefits
for object detection and instance segmentation. RP-FEM demonstrates its
capacity to suppress improbable class predictions within the image while also
preventing the model from generating duplicate predictions, leading to
improvements over the baseline model on which it is built.