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Towards Better User Studies in Computer Graphics and Vision.

Authors :
Bylinskii, Zoya
Herman, Laura
Hertzmann, Aaron
Hutka, Stefanie
Zhang, Yile
Source :
Foundations & Trends in Computer Graphics & Vision; 2023, Vol. 15 Issue 3, p201-252, 52p
Publication Year :
2023

Abstract

Online crowdsourcing platforms have made it increasingly easy to perform evaluations of algorithm outputs with survey questions like "which image is better, A or B?", leading to their proliferation in vision and graphics research papers. Results of these studies are often used as quantitative evidence in support of a paper's contributions. On the one hand we argue that, when conducted hastily as an afterthought, such studies lead to an increase of uninformative, and, potentially, misleading conclusions. On the other hand, in these same communities, user research is underutilized in driving project direction and forecasting user needs and reception. We call for increased attention to both the design and reporting of user studies in computer vision and graphics papers towards (1) improved replicability and (2) improved project direction. Together with this call, we offer an overview of methodologies from user experience research (UXR), human-computer interaction (HCI), and applied perception to increase exposure to the available methodologies and best practices. We discuss foundational user research methods (e.g., needfinding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction. We provide further pointers to the literature for readers interested in exploring other UXR methodologies. Finally, we describe broader open issues and recommendations for the research community. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15722759
Volume :
15
Issue :
3
Database :
Complementary Index
Journal :
Foundations & Trends in Computer Graphics & Vision
Publication Type :
Academic Journal
Accession number :
163579216
Full Text :
https://doi.org/10.1561/0600000106