| Lecturer Profile |
Xiaogang Wang received his Bachelor degree in Electronic Engineering and Information Science from the Special Class of Gifted Young at the University of Science and Technology of China in 2001, M. Phil. degree in Information Engineering from the Chinese University of Hong Kong in 2004, and PhD degree in Computer Science from Massachusetts Institute of Technology in 2009. He is an associate professor in the Department of Electronic Engineering at the Chinese University of Hong Kong since August 2009. He received the Outstanding Young Researcher in Automatic Human Behaviour Analysis Award in 2011, Hong Kong RGC Early Career Award in 2012, and Young Researcher Award of the Chinese University of Hong Kong. He is the associate editor of the Image and Visual Computing Journal. He was the area chair of ICCV 2011 and 2015, ECCV 2014 and 2016, and ACCV 2014 and 2016. His research interests include computer vision, deep learning, crowd video surveillance, object detection, and face recognition. |
| Lecture Abstract |
Deep learning has become a major breakthrough in artificial intelligence and achieved amazing success on solving grand challenges in many fields including computer vision, speech recognition, and natural language processing. Its success benefits from big training data and super parallel computational power emerging in recent years, as well as advanced model design and training strategies. In this talk, deep learning research in the computer vision group at the Chinese University of Hong Kong (CUHK) will be introduced. Deep ID invented by the CUHK team was the first to achieve over 99% face verification accuracy on the most well-known face recognition benchmark Labeled Faces in the Wild (LFW), surpassing human performance. They won the challenge of object detection from video in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2015. The talk will start with a historical overview and introduction of deep learning. Through concrete examples on face recognition, object detection, object tracking, and crowd scene understanding, I will explain how to effectively design the architectures of deep models and learn visual feature representations. The talk is accessible to non-experts on deep learning including undergraduates. |
