[talks] Today 4:30-5:30: Deep Learning in Detection and Recognition

[Jianxiong Xiao]

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.