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This paper presents a method to predict social saliency, the likelihood of joint attention, given an input image or video by leveraging the social interaction data captured by first person cameras. Inspired by electric dipole moments, we introduce a social formation feature that encodes the geometric relationship between joint attention and its social formation. We learn this feature from the first person social interaction data where we can precisely measure the locations of joint attentiondoi:10.1109/cvpr.2015.7299110 dblp:conf/cvpr/ParkS15 fatcat:pefgtimjyzh3xh7kbuatpaf3p4