Abstract
This paper summarizes the 2021 ChaLearn Looking at People Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions (DYAD), which featured two tracks, self-reported personality recognition and behavior forecasting, both on the UDIVA v0.5 dataset. We review important aspects of this multimodal and multiview dataset consisting of 145 interaction sessions where 134 participants converse, collaborate, and compete in a series of dyadic tasks. We also detail the transcripts and body landmark annotations for UDIVA v0.5 that are newly introduced for this occasion. We briefly comment on organizational aspects of the challenge before describing each track and presenting the proposed baselines. The results obtained by the participants are extensively analyzed to bring interesting insights about the tracks tasks and the nature of the dataset. We wrap up with a discussion on challenge outcomes, and pose several questions that we expect will motivate further scientific research to better understand social cues in human-human and human-machine interaction scenarios and help build future AI applications for good.
Originalsprog | Engelsk |
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Titel | Understanding Social Behavior in Dyadic and Small Group Interactions : Proceedings of Machine Learning Research |
Redaktører | Cristina Palmero, Julio C. S. Jacques Junior, Albert Clapés, Isabelle Guyon, Wei-Wei Tu, Thomas B. Moeslund, Sergio Escalera |
Antal sider | 49 |
Forlag | MIT Press |
Publikationsdato | 2021 |
Sider | 4-52 |
Artikelnummer | 1 |
Status | Udgivet - 2021 |
Begivenhed | ChaLearn LAP Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions Workshop, DYAD 2021, held in conjunction with the International Conference on Computer Vision, ICCV 2021 - Virtual, Online Varighed: 16 okt. 2021 → … |
Konference
Konference | ChaLearn LAP Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions Workshop, DYAD 2021, held in conjunction with the International Conference on Computer Vision, ICCV 2021 |
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By | Virtual, Online |
Periode | 16/10/2021 → … |
Navn | The Proceedings of Machine Learning Research |
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Vol/bind | 173 |
ISSN | 2640-3498 |
Bibliografisk note
Publisher Copyright:© 2022 C. Palmero et al.