Towards Better User Studies in Computer Graphics and Vision

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. We argue that, when conducted hastily as an afterthought, such studies lead to an increase of uninformative, and in some cases, misleading conclusions. 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 related fields 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. We encourage authors and reviewers alike to recognize where in the project timeline a user study would be most informative, that not every research contribution requires a user study, and that a misguided emphasis on user studies can incentivise perfunctory studies.