Human action understanding is an important and hot research problem due to its wide applications in security surveillance, self-driving vehicles, robotics, and human-machine interaction. Spatio-temporal context modeling and learning is crucial for this task. In this seminar, several deep learning architectures for human action recognition will be introduced, which include networks on modeling the spatio-temporal context dependencies in action video sequences, frameworks on selecting the most important and proper context for action analysis, and mechanisms on improving robustness of deep networks by taking advantage of the spatio-temporal context information.
Jun Liu is completing his PhD study at the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He obtained the M.Sc degree in Computer Science from Fudan University, China in 2014, and the B.Eng degree in Software Engineering from Central South University, China in 2011. From 2014 to 2015, he was with Tencent, Shanghai, China. His research interests include computer vision and artificial intelligence.