Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2017

Abstract

Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domain-based theory from social psychology is a great starting point to systematically approach this problem. The theory provides coverage of all aspects of social relations and equally is concrete and predictive about the visual attributes and behaviors defining the relations included in each domain. We provide the first dataset built on this holistic conceptualization of social life that is composed of a hierarchical label space of social domains and social relations. We also contribute the first models to recognize such domains and relations and find superior performance for attribute based features. Beyond the encouraging performance of the attribute based approach, we also find interpretable features that are in accordance with the predictions from social psychology literature. Beyond our findings, we believe that our contributions more tightly interleave visual recognition and social psychology theory that has the potential to complement the theoretical work in the area with empirical and data-driven models of social life.

Keywords

Social media, social relationship, attribute recognition, social psychology

Discipline

Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): Honolulu, HI, July 21-26: Proceedings

First Page

3481

Last Page

3490

ISBN

9781538604571

Identifier

10.1109/CVPR.2017.54

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/CVPR.2017.54

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