Chalearn LAP challenges on self-reported personality recognition and non-verbal behavior forecasting during social dyadic interactions: Dataset, design, and results
Published in Understanding Social Behavior in Dyadic and Small Group Interactions, 2022
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 based on the UDIVA v0.5 dataset. The dataset consists of 145 interaction sessions where 134 participants converse, collaborate, and compete in various dyadic tasks. The paper details the newly introduced transcripts and body landmark annotations for UDIVA v0.5, reviews important aspects of the multimodal and multiview dataset, and provides insights from the challenge outcomes. The results obtained by participants are analyzed to foster further research on social cues in human-human and human-machine interactions.
Recommended citation: Cristina Palmero, German Barquero, Julio CS Jacques Junior, Albert Clapés, Johnny Núnez, 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. (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. 4-52.
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