CCRI: Planning: Planning to Develop a Body Language Dataset for the Artificial Intelligence Research Community

CISE社区研究基础设施（CCRI）计划项目旨在形成一个研究人员社区，围绕创建一个非常大规模，高质量的注释视频数据集，以供情感计算社区广泛使用，特别是用于野外人类身体表情的计算理解。肢体语言在现实世界中情感表达的计算理解是一个基础和具有挑战性的研究课题。这一技术领域的突破有可能实现大量的创新应用，包括个人助理机器人、社交机器人和多媒体信息检索。该规划项目将鼓励科学界采取多学科和整体的方法来解决肢体语言理解问题。该项目旨在深入了解基于肢体语言的情感计算和数据驱动建模的有用数据收集。最终，通过分析和利用大量精心收集的情感数据，使用最先进的机器学习和统计建模方法，未来的智能系统可以被赋予理解人类情感的新能力。这个计划项目将涉及计算机和信息科学、社会和临床心理学、统计学和数据伦理学的研究人员。将与该研究领域的一次主要国际会议一起组织一次讲习班。该小组将与相关社区联系，吸引研究人员参加研讨会。在研讨会上，将使用团队收集的初步数据集组织一场数据驱动的情感建模比赛。获奖团队将被邀请在研讨会上展示他们的成果。工作坊亦设有小组讨论和讨论环节，以征求广大社区对潜在数据基础设施的设计和实施的意见。规划工作将产生一份出版物，内容是关于研讨会上就社区对这种数据基础设施的需求达成的共识，以及关于数据收集工作各个方面的详细规范。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This CISE Community Research Infrastructure (CCRI) planning project aims to form a community of researchers galvanized around the creation of a very large scale, high-quality annotated video dataset for broad use by the affective computing community, especially for computational understanding of human bodily expressions in the wild. Computational understanding of body language for emotional expressions in real-world environments is a fundamental and challenging research topic. Breakthroughs in this technological area have the potential to enable a large number of innovative applications including personal assistant robot, social robot, and multimedia information retrieval. The planning project will encourage the scientific community to take a multidisciplinary and holistic approach to the problem of body language understanding. The project aims to gain deeper insights into useful data collection for computational and data-driven modeling of emotions based on body language. Ultimately, by analyzing and harnessing a large quantity of carefully-collected affective data, using state-of-the-art machine learning and statistical modeling methodologies, future intelligent systems can be empowered with new capabilities of understanding human emotions.This planning project will involve researchers in computer and information sciences, social and clinical psychology, statistics, and data ethics. A workshop will be organized in conjunction with one of the leading international conferences in the research area. The team will reach out to relevant communities to attract researchers to participate in the workshop. At the workshop, a data-driven emotion modeling competition will be organized using a preliminary dataset that the team has collected. Winning teams will be invited to present their results at the workshop. The workshop will also feature panels and discussion sessions to solicit input from the broader community on the design and implementation of the potential data infrastructure. The planning effort will result in a publication on the consensus reached at the workshop on the community's need for this data infrastructure and the detailed specifications on various aspects of the data collection effort.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.