Abstract: Most of us are curious about what is happening now and what will happen next. As humans, we actually have the capability of describing, recognizing and predicting human actions because we have already visually observed certain correlations between visual elements in videos and we have such knowledge. It would be interesting to know, “can we program a machine with such capability, and build an intelligent framework that can accurately make decisions based upon video data, and even to an extreme extent, based upon temporally incomplete data?” Such a framework would be of great importance because not only would it demonstrate full comprehension on temporal rhythms of human actions, it would also serve an efficient and proactive alerting of forthcoming events before they are fully executed. However, it is challenging mainly due to the lack of rich knowledge about spatial and temporal correlations between visual elements in action videos, which are crucial to understanding complex actions. In this talk, I will describe how to learn such spatiotemporal correlations from action videos and use them for prompt human action prediction.
Bio: Dr. Yu Kong is now an Assistant Professor directing the Computer Vision lab at the College of Computing and Information Sciences, Rochester Institute of Technology. He received B.Eng. degree in automation from Anhui University in 2006, and PhD degree in computer science from Beijing Institute of Technology, China, in 2012. He was a visiting student at the National Laboratory of Pattern Recognition (NLPR), Chinese Academy of Science from 2007 to 2009, and a visiting research scholar at the Department of Computer Science and Engineering, State University of New York, Buffalo in 2012, and then a postdoc in the department of Electrical and Computer Engineering, Northeastern University, Boston, MA. Dr. Kong's research interests include computer vision, machine learning, and social media analytics. His work has been publishing on top-tier conferences and transactions in the computer vision and machine learning community such as CVPR, ECCV, AAAI, T-PAMI, IJCV, etc. He won the First Place in MSR Image Recognition Challenge, 2016. He also serves as reviewers and PC members for prestige journals and conferences, including T-PAMI, T-IP, T-NNLS, T-CSVT, CVPR, AAAI, and IJCAI, etc. More information can be found on his webpage at https://people.rit.edu/yukics/.