  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.
