Dyadformer：一种用于并矢相互作用远程建模的多模态Transformer
Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactions
摘要：
由于个性化计算具有广泛的应用前景，它已成为计算机视觉领域的一个新兴课题。然而，大多数关于这一主题的作品都集中在分析个体上，即使是在应用于互动场景时，也是在短时间内。为了解决这些局限性，我们提出了Dyadformer，一种新型的多模态多学科Transformer架构，使用可变的时间窗口来模拟二元交互中的个人和人际特征，从而可以捕获长期的相互依赖关系。我们提出的跨学科层允许网络通过注意力操作显式地模拟受试者之间的交互。这种概念验证方法显示了两种相互作用物在较长时间内的多模态和联合建模如何有助于预测个体属性。使用Dyadformer，我们改进了UDIVA v0.5数据集上个体受试者的最先进的自我报告人格推断结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
