  Since the beginning of early civilizations, social relationships derived from
each individual fundamentally form the basis of social structure in our daily
life. In the computer vision literature, much progress has been made in scene
understanding, such as object detection and scene parsing. Recent research
focuses on the relationship between objects based on its functionality and
geometrical relations. In this work, we aim to study the problem of social
relationship recognition, in still images. We have proposed a dual-glance model
for social relationship recognition, where the first glance fixates at the
individual pair of interest and the second glance deploys attention mechanism
to explore contextual cues. We have also collected a new large scale People in
Social Context (PISC) dataset, which comprises of 22,670 images and 76,568
annotated samples from 9 types of social relationship. We provide benchmark
results on the PISC dataset, and qualitatively demonstrate the efficacy of the
proposed model.
