  Recent years, human-object interaction (HOI) detection has achieved
impressive advances. However, conventional two-stage methods are usually slow
in inference. On the other hand, existing one-stage methods mainly focus on the
union regions of interactions, which introduce unnecessary visual information
as disturbances to HOI detection. To tackle the problems above, we propose a
novel one-stage HOI detection approach DIRV in this paper, based on a new
concept called interaction region for the HOI problem. Unlike previous methods,
our approach concentrates on the densely sampled interaction regions across
different scales for each human-object pair, so as to capture the subtle visual
features that is most essential to the interaction. Moreover, in order to
compensate for the detection flaws of a single interaction region, we introduce
a novel voting strategy that makes full use of those overlapped interaction
regions in place of conventional Non-Maximal Suppression (NMS). Extensive
experiments on two popular benchmarks: V-COCO and HICO-DET show that our
approach outperforms existing state-of-the-arts by a large margin with the
highest inference speed and lightest network architecture. We achieved 56.1 mAP
on V-COCO without addtional input. Our code is publicly available at:
https://github.com/MVIG-SJTU/DIRV
