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 dualglance 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.
|Original language||English (US)|
|Title of host publication||Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Dec 22 2017|
|Event||16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy|
Duration: Oct 22 2017 → Oct 29 2017
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Other||16th IEEE International Conference on Computer Vision, ICCV 2017|
|Period||10/22/17 → 10/29/17|
Bibliographical noteFunding Information:
This research is supported by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centre in Singapore Funding Initiative.
© 2017 IEEE.