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There has been a recent surge of research interest in attacking the problemof social relation inference based on images. Existing works classify socialrelations mainly by creating complicated graphs of human interactions, orlearning the foreground and/or background information of persons and objects,but ignore holistic scene context. The holistic scene refers to thefunctionality of a place in images, such as dinning room, playground andoffice. In this paper, by mimicking human understanding on images, we proposean approach of \textbf{PR}actical \textbf{I}nference in \textbf{S}ocialr\textbf{E}lation (PRISE), which concisely learns interactive features ofpersons and discriminative features of holistic scenes. Technically, we developa simple and fast relational graph convolutional network to capture interactivefeatures of all persons in one image. To learn the holistic scene feature, weelaborately design a contrastive learning task based on image sceneclassification. To further boost the performance in social relation inference,we collect and distribute a new large-scale dataset, which consists of about240 thousand unlabeled images. The extensive experimental results show that ournovel learning framework significantly beats the state-of-the-art methods,e.g., PRISE achieves 6.8$\%$ improvement for domain classification in PIPAdataset.

Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation