  Human-Object Interaction (HOI) detection devotes to learn how humans interact
with surrounding objects via inferring triplets of < human, verb, object >.
However, recent HOI detection methods mostly rely on additional annotations
(e.g., human pose) and neglect powerful interactive reasoning beyond
convolutions. In this paper, we present a novel graph-based interactive
reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs, in
which interactive semantics implied among visual targets are efficiently
exploited. The proposed model consists of a project function that maps related
targets from convolution space to a graph-based semantic space, a message
passing process propagating semantics among all nodes and an update function
transforming the reasoned nodes back to convolution space. Furthermore, we
construct a new framework to assemble in-Graph models for detecting HOIs,
namely in-GraphNet. Beyond inferring HOIs using instance features respectively,
the framework dynamically parses pairwise interactive semantics among visual
targets by integrating two-level in-Graphs, i.e., scene-wide and instance-wide
in-Graphs. Our framework is end-to-end trainable and free from costly
annotations like human pose. Extensive experiments show that our proposed
framework outperforms existing HOI detection methods on both V-COCO and
HICO-DET benchmarks and improves the baseline about 9.4% and 15% relatively,
validating its efficacy in detecting HOIs.
