  While Human-Object Interaction(HOI) Detection has achieved tremendous
advances in recent, it still remains challenging due to complex interactions
with multiple humans and objects occurring in images, which would inevitably
lead to ambiguities. Most existing methods either generate all human-object
pair candidates and infer their relationships by cropped local features
successively in a two-stage manner, or directly predict interaction points in a
one-stage procedure. However, the lack of spatial configurations or reasoning
steps of two- or one- stage methods respectively limits their performance in
such complex scenes. To avoid this ambiguity, we propose a novel actor-centric
framework. The main ideas are that when inferring interactions: 1) the
non-local features of the entire image guided by actor position are obtained to
model the relationship between the actor and context, and then 2) we use an
object branch to generate pixel-wise interaction area prediction, where the
interaction area denotes the object central area. Moreover, we also use an
actor branch to get interaction prediction of the actor and propose a novel
composition strategy based on center-point indexing to generate the final HOI
prediction. Thanks to the usage of the non-local features and the
partly-coupled property of the human-objects composition strategy, our proposed
framework can detect HOI more accurately especially for complex images.
Extensive experimental results show that our method achieves the
state-of-the-art on the challenging V-COCO and HICO-DET benchmarks and is more
robust especially in multiple persons and/or objects scenes.
