UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection

Bumsoo Kim, Taeho Choi, Jaewoo Kang, Hyunwoo J. Kim ;

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 first, fastest and best performing one-stage detector for human-object interaction shows a significant reduction in interaction prediction time ($4 imes \sim 14 imes$) while outperforming state-of-the-art methods on two public datasets: V-COCO and HICO-DET."