We introduce DAiSEE, the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration in the wild. The dataset has four levels of labels namely - very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. We have also established benchmark results on this dataset using state-of-the-art video classification methods that are available today. We believe that DAiSEE will provide the research community with challenges in feature extraction, context-based inference, and development of suitable machine learning methods for related tasks, thus providing a springboard for further research. The dataset is available for download at https://iith.ac.in/~daisee-dataset.

翻译：我们引入了第一个多标签视频分类数据集DAiSEE,这是第一个由112个用户收集的9068个视频片段组成的多标签视频分类数据集,用于识别野生无聊、混乱、参与和沮丧的用户感官状态。该数据集有四个等级的标签,即每个受访州非常低、低、高、高,而且非常高。这些标签与使用专家心理学家小组创建的黄金标准注释有关。我们还利用当今最先进的视频分类方法确定了该数据集的基准结果。我们相信DAiSEE将为研究界提供特征提取、基于背景的推断以及开发相关任务的适当机器学习方法方面的挑战,从而为进一步研究提供一个跳板。数据集可在https://ii.ac.in/~daise-dataset下载。