  Recent advances in deep neural networks have achieved significant progress in
detecting individual objects from an image. However, object detection is not
sufficient to fully understand a visual scene. Towards a deeper visual
understanding, the interactions between objects, especially humans and objects
are essential. Most prior works have obtained this information with a bottom-up
approach, where the objects are first detected and the interactions are
predicted sequentially by pairing the objects. This is a major bottleneck in
HOI detection inference time. To tackle this problem, we propose UnionDet, a
one-stage meta-architecture for HOI detection powered by a novel union-level
detector that eliminates this additional inference stage by directly capturing
the region of interaction. Our one-stage detector for human-object interaction
shows a significant reduction in interaction prediction time 4x~14x while
outperforming state-of-the-art methods on two public datasets: V-COCO and
HICO-DET.
