  In this paper, we investigate the use of an unsupervised label clustering
technique and demonstrate that it enables substantial improvements in visual
relationship prediction accuracy on the Person in Context (PIC) dataset. We
propose to group object labels with similar patterns of relationship
distribution in the dataset into fewer categories. Label clustering not only
mitigates both the large classification space and class imbalance issues, but
also potentially increases data samples for each clustered category. We further
propose to incorporate depth information as an additional feature into the
instance segmentation model. The additional depth prediction path supplements
the relationship prediction model in a way that bounding boxes or segmentation
masks are unable to deliver. We have rigorously evaluated the proposed
techniques and performed various ablation analysis to validate the benefits of
them.
