论文标题
Avcaffe：认知负载的大规模视听数据集和远程工作的影响
AVCAffe: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect for Remote Work
论文作者
论文摘要
我们介绍了Avcaffe，这是第一个由认知负载和影响属性组成的视听数据集。我们通过模拟视频会议平台模拟远程工作方案来录制Avcaffe，主题协作完成了许多认知引人入胜的任务。 Avcaffe是最初收集的（未从互联网收集）的英语情感数据集。我们招募了来自18个不同原籍国的106名参与者，跨越了18至57岁的年龄范围，男性女性比例均衡。 Avcaffe总共包括108小时的视频，相当于58,000多个剪辑，以及基于任务的自我报告的地面真相标签，用于唤醒，价值和认知负载属性，例如精神需求，时间需求，努力以及其他一些。我们认为，鉴于对深度学习研究社区的固有困难和认知负担的固有困难，Avcaffe对于深度学习研究界来说将是一个具有挑战性的基准。此外，我们的数据集通过促进学习系统的创建来更好地自我管理远程工作会议，并进一步研究有关远程工作对认知负荷和情感状态的影响的假设，从而填补了现有的及时差距。
We introduce AVCAffe, the first Audio-Visual dataset consisting of Cognitive load and Affect attributes. We record AVCAffe by simulating remote work scenarios over a video-conferencing platform, where subjects collaborate to complete a number of cognitively engaging tasks. AVCAffe is the largest originally collected (not collected from the Internet) affective dataset in English language. We recruit 106 participants from 18 different countries of origin, spanning an age range of 18 to 57 years old, with a balanced male-female ratio. AVCAffe comprises a total of 108 hours of video, equivalent to more than 58,000 clips along with task-based self-reported ground truth labels for arousal, valence, and cognitive load attributes such as mental demand, temporal demand, effort, and a few others. We believe AVCAffe would be a challenging benchmark for the deep learning research community given the inherent difficulty of classifying affect and cognitive load in particular. Moreover, our dataset fills an existing timely gap by facilitating the creation of learning systems for better self-management of remote work meetings, and further study of hypotheses regarding the impact of remote work on cognitive load and affective states.
