The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a machine is referred to as `user engagement'. Engagement comprises of behavioural, cognitive, and affect related cues. To create engagement predictions systems, which can work in real-world conditions it is quintessential to learn from rich diverse datasets. To this end, a large scale multi-faceted engagement in the wild dataset is proposed. 31 hours duration data of 127 participants representing different illumination conditions is recorded. Thorough experiments are performed exploring applicability of different features action units, eye gaze and head pose and transformers. To further validate the rich nature of the dataset, evaluation is also performed on the EngageWild dataset. The experiments show the usefulness of the proposed dataset. The code, models and dataset will be made publicly available.
翻译:个人在与机器互动时所表现出的集中、热情、乐观和激情的程度被称为“用户参与”。参与包括行为、认知和影响相关提示。创建参与预测系统,可以在现实世界条件下发挥作用。为此,提议在野生数据集中进行大规模多面参与。记录了代表不同发光条件的127名参与者的31小时持续时间数据。进行细微实验,探索不同功能动作单元、眼视、头部姿势和变压器的适用性。为了进一步验证数据集的丰富性质,还在 " BengeWild数据集 " 上进行评估。实验显示拟议的数据集的有用性。代码、模型和数据集将公开提供。