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.

这篇论文介绍了UDIVA，一个新的面对面二人交互非活跃数据集，其中的交互涉及合作和竞争任务，包含了不同行为引发和认知负荷的因素，研究探讨了基于Transformers的音频和视觉数据的自报个人特质推断的方法及使用多种上下文信息进行推断的效果分析。

双人场景下的上下文感知人格推断：UDIVA数据集介绍