@InProceedings{CristinaPalmero2021,
author="Cristina Palmero
and Javier Selva
and Sorina Smeureanu
and Julio C. S. Jacques Junior
and Albert Clapes
and Alexa Mosegui
and Zejian Zhang
and David Gallardo
and Georgina Guilera
and David Leiva
and Sergio Escalera",
title="Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset",
booktitle="IEEE Winter Conference on Applications of Computer Vision",
year="2021",
pages="1--12",
abstract="This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose atransformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors toregress a target person{\textquoteright}s personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information.",
optnote="HUPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3532), last updated on Mon, 24 Oct 2022 13:43:32 +0200",
doi="10.1109/WACVW52041.2021.00005",
file=":http://refbase.cvc.uab.es/files/PSS2021.pdf:PDF"
}