Fine-grained Human Action Recognition with Deep Graph Neural Networks

该项目旨在开发新的基于图神经网络的深度学习算法，用于细粒度的人类行为识别。该项目期望将人类行为分析提升到一个新的水平，并显著推进对微妙而复杂的人类行为的分析。该项目的预期成果包括基于图表示的时空数据深度学习算法的理论进展，以及在许多领域（如体育和健康）实现更客观的人类行为分析的技术。这将为任何涉及大而复杂的时空数据的应用领域提供显著的好处，以便进行更精细的分析和更好的知识发现。

This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and enabling techniques for more objective human action analysis in many domains such as sports and health. This should provide significant benefits to any application domain involving big and complex spatial-temporal data for finer analytics and better knowledge discovery.