Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present the Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions using variable time windows, thus allowing the capture of long-term interdependencies. Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations. This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes. With Dyadformer, we improve state-of-the-art self-reported personality inference results on individual subjects on the UDIVA v0.5 dataset.

本研究介绍了Dyadformer这个多模态多对象 Transformer 模型，用于对长时期互动中的个人和人际特征进行建模，从而改善了单独分析个体的限制，并通过注意力操作显式地建模了对象之间的交互作用，从而提高了UDIVA v0.5数据集上个人属性的推断准确度。

Dyadformer：一种用于二元交互远程建模的多模态Transformer