推论
人格
计算机科学
五大性格特征
仿形(计算机编程)
背景(考古学)
机器学习
认知
人工智能
认知心理学
心理学
社会心理学
生物
操作系统
古生物学
神经科学
作者
Cristina Palmero,Javier Selva,Sorina Smeureanu,Julio C. S. Jacques,Albert Clapés,Alexa Moseguí,Zejian Zhang,David Gallardo,Georgina Guilera,David Leiva,Sérgio Escalera
标识
DOI:10.1109/wacvw52041.2021.00005
摘要
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 a transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to regress a target person's personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information.
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