  There has been a recent surge of research interest in attacking the problem
of social relation inference based on images. Existing works classify social
relations mainly by creating complicated graphs of human interactions, or
learning the foreground and/or background information of persons and objects,
but ignore holistic scene context. The holistic scene refers to the
functionality of a place in images, such as dinning room, playground and
office. In this paper, by mimicking human understanding on images, we propose
an approach of \textbf{PR}actical \textbf{I}nference in \textbf{S}ocial
r\textbf{E}lation (PRISE), which concisely learns interactive features of
persons and discriminative features of holistic scenes. Technically, we develop
a simple and fast relational graph convolutional network to capture interactive
features of all persons in one image. To learn the holistic scene feature, we
elaborately design a contrastive learning task based on image scene
classification. To further boost the performance in social relation inference,
we collect and distribute a new large-scale dataset, which consists of about
240 thousand unlabeled images. The extensive experimental results show that our
novel learning framework significantly beats the state-of-the-art methods,
e.g., PRISE achieves 6.8$\%$ improvement for domain classification in PIPA
dataset.
