Topic：Deep Learning in Object Detection,

 Segmentation and Recognition

Time：2013年11月14日（周四）下午2:00 - 4:00

Venue：信电大楼215学术厅

Speaker：Xiaogang Wang, Assistant Professor,

  The Chinese University of Hong Kong

Biography

Xiaogang Wang received his Bachelor degree in Electrical 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 assistant 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, ECCV 2014 and ACCV 2014. His research interests include computer vision, deep learning, crowd video surveillance, object detection, and face recognition.

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. In this seminar, I will introduce our recent works on developing deep models to solve several computer vision problems, including pedestrian detection, facial keypoint detection, face parsing, pedestrian parsing, face recognition, and face attribute recognition. Deep models significantly advance the state-of-the-art on these challenges because of their capability of automatically learning hierarchical feature representations from data, jointly optimizing key components in a computer vision system, and their learning capacity. Through examples, I will share our experience on how to formulate a vision problem with deep learning, how to train a deep neural network, how to make use the large learning capacity of deep models, and how to learn features in a vision application. The benefits of deep architectures will also be discussed.