Human beings are fundamentally sociable -- that we generally organize our
social lives in terms of relations with other people. Understanding social
relations from an image has great potential for intelligent systems such as
social chatbots and personal assistants. In this paper, we propose a simpler,
faster, and more accurate method named graph relational reasoning network
(GR2N) for social relation recognition. Different from existing methods which
process all social relations on an image independently, our method considers
the paradigm of jointly inferring the relations by constructing a social
relation graph. Furthermore, the proposed GR2N constructs several virtual
relation graphs to explicitly grasp the strong logical constraints among
different types of social relations. Experimental results illustrate that our
method generates a reasonable and consistent social relation graph and improves
the performance in both accuracy and efficiency.

本文提出了一种基于图形关系推理网络方法 (GR2N) 的社交关系识别方法，该方法相比现有方法能够更快，更准确地构建社交关系图，并显式地抓住不同类型社交关系之间的强逻辑约束，从而提高社交关系图的性能。