Human-object interaction(HOI) detection is an important task for
understanding human activity. Graph structure is appropriate to denote the HOIs
in the scene. Since there is an subordination between human and object---human
play subjective role and object play objective role in HOI, the relations
between homogeneous entities and heterogeneous entities in the scene should
also not be equally the same. However, previous graph models regard human and
object as the same kind of nodes and do not consider that the messages are not
equally the same between different entities. In this work, we address such a
problem for HOI task by proposing a heterogeneous graph network that models
humans and objects as different kinds of nodes and incorporates intra-class
messages between homogeneous nodes and inter-class messages between
heterogeneous nodes. In addition, a graph attention mechanism based on the
intra-class context and inter-class context is exploited to improve the
learning. Extensive experiments on the benchmark datasets V-COCO and HICO-DET
demonstrate that the intra-class and inter-class messages are very important in
HOI detection and verify the effectiveness of our method.

本文提出一种基于异构图网络的目标与人交互检测方法，通过将人和物体建模为不同种类的节点，利用节点之间的内部关联信息和跨类别信息，再利用图注意力机制提升学习效果，实验表明内部关系和跨类别信息对于目标与人交互检测非常重要，并证实了模型的有效性。