Barquero, German

Clapés, Albert

Curto, David

Escalera, Sergio

Feng, Xiaoxue

Gallardo-Pujol, David

Guilera, Georgina

Guyon, Isabelle

Han, Feng

He, Jennifer

Jacques Junior, Julio C. S.

Leiva, David

Moeslund, Thomas B.

Núñez, Johnny

Palmero, Cristina

Saeteros, David

Selva, Javier

Smeureanu, Sorina

Tu, Wei-Wei

Zhang, Zejian

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.</p

Jacques Junior, Julio C.S.

Tu, Wei Wei

VBN (Videnbasen)  Aalborg Universitets forskningsportal

English

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

Chalearn LAP challenges on self-reported personality recognition and non-verbal behavior forecasting during social dyadic interactions: Dataset, design, and results

Chalearn LAP challenges on self-reported personality recognition and non-verbal behavior forecasting during social dyadic interactions: Dataset, design, and results

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VBN (Videnbasen) Aalborg Universitets forskningsportal