Towards data-efficient future action prediction in the wild

实现数据高效的野外未来行动预测

• 批准号: DE190100626
• 负责人: Prof Xiaojun Chang
• 金额: $ 27.59万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2019
• 资助国家: 澳大利亚
• 起止时间: 2019-05-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE190100626
• 关键词: Towards; data; efficient; future; action

This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generation of intelligent systems to accommodate limited supervision. This should benefit science, society, and the economy nationally through the applications of autonomous vehicles, sensor technologies, and cybersecurity.

该项目旨在构建最先进的深度学习模型，以预测视频中的未来行动。该项目预计将产生机器智能的下一个伟大步骤，即有可能探索少数已标记的例子，以更好地理解、解释和推断人类的行为。该项目的预期结果为学习未来在野外场景中的行动预测奠定了理论基础，并构建了适应有限监管的下一代智能系统。这将通过自动驾驶汽车、传感器技术和网络安全的应用，在全国范围内造福于科学、社会和经济。