  Comprehensive visual understanding requires detection frameworks that can
effectively learn and utilize object interactions while analyzing objects
individually. This is the main objective in Human-Object Interaction (HOI)
detection task. In particular, relative spatial reasoning and structural
connections between objects are essential cues for analyzing interactions,
which is addressed by the proposed Visual-Spatial-Graph Network (VSGNet)
architecture. VSGNet extracts visual features from the human-object pairs,
refines the features with spatial configurations of the pair, and utilizes the
structural connections between the pair via graph convolutions. The performance
of VSGNet is thoroughly evaluated using the Verbs in COCO (V-COCO) and HICO-DET
datasets. Experimental results indicate that VSGNet outperforms
state-of-the-art solutions by 8% or 4 mAP in V-COCO and 16% or 3 mAP in
HICO-DET.
