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Discovering Hierarchical Representations for Action Understanding

发现动作理解的层次表示

基本信息

批准号：
1655300
负责人：
Hongjing Lu
金额：
$ 55.58万
依托单位：
University of California-Los Angeles
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1655300&HistoricalAwards=false
关键词：
Discovering Hierarchical Representations Action Understanding

项目摘要

A major issue in the psychological sciences is understanding how people can infer the intentions of others. Humans are remarkably adept at predicting the actions of other people and making inferences about their intention and goals. The present investigation examines how humans make such inferences from the physical movements of others. The work is guided by a computational theory of biological motion understanding that quantifies what aspects of actions allow observers to make inferences about the meaning of actions and what might come next. The larger goal is to explain how perception and reasoning operate synergistically to infer hidden goals and intentions. These findings will guide development of the next generation of intelligent machine-vision systems, useful in forensic sciences as well as many other real-world applications. Such systems will need to perform challenging tasks that currently are difficult and time-consuming for humans (for example, automated interpretation of human actions recorded in low-resolution surveillance video). The project will also help to identify individual differences in action understanding, potentially revealing the nature of the impairments in action understanding observed in people with autism disorder. In addition, the project will provide a unique training opportunity for students who are interested in interdisciplinary research at the interface between cognitive science and artificial intelligence and will provide an in-depth international research experience for a graduate student and postdoctoral fellow.The research will integrate advanced psychophysical methods with sophisticated computational approaches. A key aim is to develop a unified theory based on a hierarchical non-parametric Bayesian framework, specifying the fundamental computational mechanisms involved in perception of human actions and reasoning about them. More generally, the project will use human body movements as an underutilized approach to understanding general problems in learning: how to construct, use and transform hierarchical representations to support human perception and cognition. Three aims are particularly noteworthy. First, the project will integrate computational modeling approaches with behavioral experiments to investigate the critical connection between perceptual and cognitive systems. Second, the project uses action stimuli derived from motion capture data in the real world as the visual input (CCTV images collected in the UK and secured at the University of Glasgow). By avoiding the limitations of studies that use restricted examples and constrained environments, the investigators maximize the likelihood that the findings will generalize to real-world situations. Third, the project will develop significant extensions of Bayesian approaches in order to study complex visual processes by combining generative models with probabilistic constraints. This award is co-funded by the Perception, Action, and Cognition Program and the Office of International Science and Engineering.

心理科学的一个主要问题是理解人们如何推断他人的意图。人类非常擅长预测他人的行为，并推断他们的意图和目标。目前的调查研究了人类如何从他人的身体动作中做出这样的推断。这项工作以生物运动理解的计算理论为指导，该理论量化了行动的哪些方面可以让观察者对行动的意义和下一步可能发生的事情做出推断。更大的目标是解释感知和推理如何协同运作来推断隐藏的目标和意图。这些发现将指导下一代智能机器视觉系统的发展，在法医科学以及许多其他现实世界的应用中都很有用。此类系统将需要执行目前对人类来说困难且耗时的挑战性任务（例如，自动解释低分辨率监控视频中记录的人类行为）。该项目还将有助于确定行动理解的个体差异，潜在地揭示在自闭症患者中观察到的行动理解障碍的本质。此外，该项目将为对认知科学与人工智能交叉领域研究感兴趣的学生提供独特的培训机会，并将为研究生和博士后提供深入的国际研究经验。这项研究将结合先进的心理物理方法和复杂的计算方法。一个关键目标是建立一个基于分层非参数贝叶斯框架的统一理论，指定涉及人类行为感知和推理的基本计算机制。更一般地说，该项目将使用人体运动作为一种未被充分利用的方法来理解学习中的一般问题：如何构建、使用和转换分层表示来支持人类感知和认知。有三个目标特别值得注意。首先，该项目将结合计算建模方法和行为实验来研究感知和认知系统之间的关键联系。其次，该项目使用来自现实世界中动作捕捉数据的动作刺激作为视觉输入（CCTV图像收集于英国，并在格拉斯哥大学获得）。通过避免使用受限示例和受限环境的研究的局限性，研究人员最大限度地提高了研究结果推广到现实世界的可能性。第三，该项目将开发贝叶斯方法的重要扩展，以便通过将生成模型与概率约束相结合来研究复杂的视觉过程。该奖项由感知、行动和认知项目和国际科学与工程办公室共同资助。