  Human-Object Interaction (HOI) detection devotes to learn how humans interact
with surrounding objects. Latest end-to-end HOI detectors are short of relation
reasoning, which leads to inability to learn HOI-specific interactive semantics
for predictions. In this paper, we therefore propose novel relation reasoning
for HOI detection. We first present a progressive Relation-aware Frame, which
brings a new structure and parameter sharing pattern for interaction inference.
Upon the frame, an Interaction Intensifier Module and a Correlation Parsing
Module are carefully designed, where: a) interactive semantics from humans can
be exploited and passed to objects to intensify interactions, b) interactive
correlations among humans, objects and interactions are integrated to promote
predictions. Based on modules above, we construct an end-to-end trainable
framework named Relation Reasoning Network (abbr. RR-Net). Extensive
experiments show that our proposed RR-Net sets a new state-of-the-art on both
V-COCO and HICO-DET benchmarks and improves the baseline about 5.5% and 9.8%
relatively, validating that this first effort in exploring relation reasoning
and integrating interactive semantics has brought obvious improvement for
end-to-end HOI detection.
