ChaLearn LAP Challenges on Self-Reported Personality Recognition and Non-Verbal Behavior Forecasting During Social Dyadic Interactions: Dataset, Design, and Results

Cristina Palmero, German Barquero, Julio C. S. Jacques Junior, Albert Clapés, Johnny Núñez, David Curto, Sorina Smeureanu, Javier Selva, Zejian Zhang, David Saeteros, David Gallardo-Pujol, Georgina Guilera, David Leiva, Feng Han, Xiaoxue Feng, Jennifer He, Wei-Wei Tu, Thomas B. Moeslund, Isabelle Guyon, Sergio Escalera
Understanding Social Behavior in Dyadic and Small Group Interactions, PMLR 173:4-52, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v173-palmero22b, title = {ChaLearn {LAP} Challenges on Self-Reported Personality Recognition and Non-Verbal Behavior Forecasting During Social Dyadic Interactions: Dataset, Design, and Results}, author = {Palmero, Cristina and Barquero, German and Jacques Junior, Julio C. S. and Clap{\'e}s, Albert and N{\'u}{\~n}ez, Johnny and Curto, David and Smeureanu, Sorina and Selva, Javier and Zhang, Zejian and Saeteros, David and Gallardo-Pujol, David and Guilera, Georgina and Leiva, David and Han, Feng and Feng, Xiaoxue and He, Jennifer and Tu, Wei-Wei and Moeslund, Thomas B. and Guyon, Isabelle and Escalera, Sergio}, booktitle = {Understanding Social Behavior in Dyadic and Small Group Interactions}, pages = {4--52}, year = {2022}, editor = {Palmero, Cristina and Jacques Junior, Julio C. S. and Clapés, Albert and Guyon, Isabelle and Tu, Wei-Wei and Moeslund, Thomas B. and Escalera, Sergio}, volume = {173}, series = {Proceedings of Machine Learning Research}, month = {16 Oct}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v173/palmero22b/palmero22b.pdf}, url = {https://proceedings.mlr.press/v173/palmero22b.html}, 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.} }
Endnote
%0 Conference Paper %T ChaLearn LAP Challenges on Self-Reported Personality Recognition and Non-Verbal Behavior Forecasting During Social Dyadic Interactions: Dataset, Design, and Results %A Cristina Palmero %A German Barquero %A Julio C. S. Jacques Junior %A Albert Clapés %A Johnny Núñez %A David Curto %A Sorina Smeureanu %A Javier Selva %A Zejian Zhang %A David Saeteros %A David Gallardo-Pujol %A Georgina Guilera %A David Leiva %A Feng Han %A Xiaoxue Feng %A Jennifer He %A Wei-Wei Tu %A Thomas B. Moeslund %A Isabelle Guyon %A Sergio Escalera %B Understanding Social Behavior in Dyadic and Small Group Interactions %C Proceedings of Machine Learning Research %D 2022 %E Cristina Palmero %E Julio C. S. Jacques Junior %E Albert Clapés %E Isabelle Guyon %E Wei-Wei Tu %E Thomas B. Moeslund %E Sergio Escalera %F pmlr-v173-palmero22b %I PMLR %P 4--52 %U https://proceedings.mlr.press/v173/palmero22b.html %V 173 %X 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.
APA
Palmero, C., Barquero, G., Jacques Junior, J.C.S., Clapés, A., Núñez, J., Curto, D., Smeureanu, S., Selva, J., Zhang, Z., Saeteros, D., Gallardo-Pujol, D., Guilera, G., Leiva, D., Han, F., Feng, X., He, J., Tu, W., Moeslund, T.B., Guyon, I. & Escalera, S.. (2022). ChaLearn LAP Challenges on Self-Reported Personality Recognition and Non-Verbal Behavior Forecasting During Social Dyadic Interactions: Dataset, Design, and Results. Understanding Social Behavior in Dyadic and Small Group Interactions, in Proceedings of Machine Learning Research 173:4-52 Available from https://proceedings.mlr.press/v173/palmero22b.html.

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