Speaker: Xiaogang Wang Room: CS 402 Time: July 2nd Wednesday 4:30-5:30 Title: Deep Learning in Detection and Recognition Abstract: In this seminar, I will introduce our recent works on developing deep models to solve several computer vision problems, especially focusing on pedestrian detection and face 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, disentangling hidden factors, 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, and how to effectively train a deep neural network. Instead of treating a deep model as a black box, we investigate the connection between deep models and existing vision systems, such that a lot of insights and experience accumulated from vision research can be used to develop new deep models and effective training strategies. Taking pedestrian detection as an example, pedestrian detection, new layers are designed to function as part detectors, modeling deformations and inferring the visibility of body parts. I will also report our results on face recognition which can be addressed by deep learning in several different ways. Bio: 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.