Prof. Wan-Chi Siu
IEEE Life Fellow & IET Fellow
Hong Kong Polytechnic University, Hong Kong,
China
Wan-Chi Siu
(S’77-M’77-SM’90-F’12-Life-F’16) received the
MPhil and PhD degrees from The Chinese
University of Hong Kong in 1977 and Imperial
College London in 1984. He is Life-Fellow of
IEEE and Fellow of IET, and Immediate-Past
President (2019-2020) of APSIPA (Asia-Pacific
Signal and Information Processing Association).
Prof. Siu is now Emeritus Professor, and was
Chair Professor, Founding Director of Signal
Processing Research Centre, Head of Electronic
and Information Engineering Department and Dean
of Engineering Faculty of The Hong Kong
Polytechnic University. He is an expert in DSP,
transforms, fast algorithms, machine learning,
and conventional and deep learning approaches
for super-resolution imaging, 2D and 3D video
coding, object recognition and tracking. He has
published 500 research papers (over 200 appeared
in international journal papers), and edited
three books. He has also 9 recent patents
granted. Prof. Siu was an independent
non-executive director (2000-2015) of a
publicly-listed video surveillance company and
convenor of the First Engineering/IT Panel of
the RAE(1992/93) in Hong Kong. He is an
outstanding scholar, with many awards, including
the Best Teacher Award, the Best Faculty
Researcher Award (twice) and IEEE Third
Millennium Medal (2000). Prof. Siu has been
Guest Editor/Subject Editor/AE for IEEE
Transactions on Circuits and System II, Image
Processing, Circuit & System for Video
Technology, and Electronics Letters, and
organized very successfully over 20
international conferences including IEEE
society-sponsored flagship conferences, such as
TPC Chair of ISCAS1997 and General Chair of
ICASSP2003 and General Chair of ICIP2010. He was
Vice-President, Chair of Conference Board and
Core Member of Board of Governors (2012-2014) of
the IEEE Signal Processing Society, and has been
a member of the IEEE Educational Activities
Board, IEEE Fourier Award for Signal Processing
Committee (2017-2020) and some other IEEE
Technical Committees.
Speech Title: Deep Learning Baseline Model Design for Image Enlightening and Super-Resolution
Abstract:
There are always large demands for more suitable
and efficient machine learning techniques for
hi-tech applications. In this talk we will start
with a brief review of the architecture of a
standard deep learning network for
classification and image manipulation
applications. Many possible approaches can
possibly be used to achieve improvement of the
architecture of the deep learning structure,
which include making novel improvement on the
design of the baseline model, information
aggregation, resolving the conflict between
optimization and generalization and
normalization approach of deep learning
algorithms. For the present presentation, we
will just concentrate on the study of baseline
models for deep learning, and will give a brief
discussion on the evolution of building blocks
for deep learning architectures. We will then
proceed with the discussion of our proposed
baseline models making use of joint back
projection and residual network. Network design
for applications in super-resolution imaging and
image enlightening will be discussed. The
techniques can be used as a reference for those
who want to design their own deep learning
networks for specific applications.
Demonstrations and experimental results will be
provided to show the effect of the new design.
At the end of the talk we will also discuss
briefly other possible ways for architectural
improvement, and research trend along this
direction.
Prof. Weisi Lin
IEEE
Fellow & IET Fellow
Nanyang Technological University, Singapore
Dr. Weisi Lin is an active researcher in image
processing, perception-based signal modelling
and assessment, video compression, and
multimedia communication systems. In the said
areas, he has published 180+ international
journal papers and 230+ international conference
papers, 7 patents, 9 book chapters, 2 authored
books and 3 edited books, as well as excellent
track record in leading and delivering more than
10 major funded projects (with over S$7m
research funding). He earned his BSc and MSc
from Sun Yat-Sen University, China, and Ph.D
from King’s College, University of London. He
had been the Lab Head, Visual Processing,
Institute for Infocomm Research (I2R). He is a
Professor in School of Computer Science and
Engineering, Nanyang Technological University,
where he also serves as the Associate Chair
(Research).
He is a Fellow of IEEE and IET, and an Honorary
Fellow of Singapore Institute of Engineering
Technologists. He has been awarded Highly Cited
Researcher 2019 by Web of Science, and elected
as a Distinguished Lecturer in both IEEE
Circuits and Systems Society (2016-17) and
Asia-Pacific Signal and Information Processing
Association (2012-13), and given
keynote/invited/tutorial/panel talks to 20+
international conferences during the past 10
years. He has been an Associate Editor for IEEE
Trans. on Image Processing, IEEE Trans. on
Circuits and Systems for Video Technology, IEEE
Trans. on Multimedia, IEEE Signal Processing
Letters, Quality and User Experience, and
Journal of Visual Communication and Image
Representation. He was also the Guest Editor for
7 special issues in international journals, and
chaired the IEEE MMTC QoE Interest Group
(2012-2014); he has been a Technical Program
Chair for IEEE Int’l Conf. Multimedia and Expo
(ICME 2013), International Workshop on Quality
of Multimedia Experience (QoMEX 2014),
International Packet Video Workshop (PV 2015),
Pacific-Rim Conf. on Multimedia (PCM 2012) and
IEEE Visual Communications and Image Processing
(VCIP 2017). He believes that good theory is
practical, and has delivered 10+ major systems
and modules for industrial deployment with the
technology develloped.
Speech Title: From Video Coding to Visual Feature Coding: toward Collaborative Intelligence & Beyond
Abstract:
Great success has been achieved in image and
video coding during the past 3 decades thanks to
joint effort of academia and industries,
resulting in countless products and services.
The ubiquity of images and videos enabled by the
coding technology no doubt has significantly
contributed to the huge impact leading to the
award of 2009 Nobel Prize in Physics to the CCD
camera inventors.
It is expected the further exploration in this
area to be along 2 major directions: with humans
and machines as the ultimate users,
respectively. Firstly in this talk, several new
paradigms are to be explored to compress whole
visual signals, along the 1st direction since
the human being continues to use more and more
images and videos. After 30+ years’ intensive
development and optimization, the room for
further improvement is diminishing with the
existing hybrid coding framework; we will
discuss some out-of-the-box approaches,
including synthetic frame formation, alternative
transforms, just-noticeable-difference guided
coding, fine-grained quality evaluation, and so
on.
The second part of this talk is devoted to the
aforementioned 2nd direction, since machines
increasingly become the ultimate users for
visual signals in the AI era. We explore for
intermediate deep-learnt visual features (rather
than whole image/video) to be coded in response
to the challenges of collaborative intelligence
(CI) between edge and cloud/clients, and
facilitate integration of signal compression and
machine vision (being separate tasks
traditionally), accurate feature extraction,
privacy preservation, flexible load distribution
and power/battery reduction. It is hoped that
the presentation can trigger more R&D in the
related fields, inclusive of collaborative human
and artificial intelligence.
Prof. Ioannis Pitas
IEEE Fellow
Aristotle University of Thessaloniki, Greece
Prof. Ioannis Pitas (IEEE fellow, IEEE
Distinguished Lecturer, EURASIP fellow) received
the Diploma and PhD degree in Electrical
Engineering, both from the Aristotle University
of Thessaloniki (AUTH), Greece. Since 1994, he
has been a Professor at the Department of
Informatics of AUTH and Director of the
Artificial Intelligence and Information Analysis
(AIIA) lab. He served as a Visiting Professor at
several Universities.
His current interests are in the areas of
computer vision, machine learning, autonomous
systems, intelligent digital media, image/video
processing, human-centred interfaces, affective
computing, 3D imaging and biomedical imaging. He
has published over 906 papers, contributed in 47
books in his areas of interest and edited or
(co-)authored another 11 books. He has also been
member of the program committee of many
scientific conferences and workshops. In the
past he served as Associate Editor or co-Editor
of 9 international journals and General or
Technical Chair of 4 international conferences.
He participated in 70 R&D projects, primarily
funded by the European Union and is/was
principal investigator/researcher in 42 such
projects. He has 31200+ citations to his work
and h-index 84+ (Google Scholar).
Prof. Pitas lead the big European H2020 R&D
project MULTIDRONE:
https://multidrone.eu/. He is AUTH principal
investigator in H2020 R&D projects Aerial Core
and AI4Media. He is chair of the Autonomous
Systems Initiative
https://ieeeasi.signalprocessingsociety.org/.
He is head of the EC funded AI doctoral school
of Horizon2020 EU funded R&D project AI4Media (1
of the 4 in Europe).
Speech Title: Generative Adversarial
Networks in Multimedia Content Creation
Abstract: Deep Convolutional Generative
Adversarial Networks (DCGAN) have been used to
generate highly compelling pictures or videos,
such as manipulated facial animations, interior
and outdoor images, videos. This lecture
provides an extensive overview of several
Generative Adversarial Networks applications for
media production, notably for image content
generation (e.g., human facial and body images),
automatic image
restyling/translation/captioning, text to image
synthesis, video frame prediction, video content
generation (e.g., human animations), automatic
audio-visual content captioning. If this trend
does indeed succeed, it will revolutionize arts
and media production.
