Prof.
Sam Kwong
IEEE Fellow
City University of Hong Kong, Hong Kong, China
Sam Kwong received his B.Sc. degree from the State
University of New York at Buffalo, M.A.Sc. in electrical
engineering from the University of Waterloo in Canada,
and Ph.D. from Fernuniversität Hagen, Germany. Kwong is
currently a Chair Professor at the CityU Department of
Computer Science, where he previously served as
Department Head and Professor from 2012 to 2018. Prof.
Kwong is the associate editor of leading IEEE
transaction journals, including IEEE Transactions on
Evolutionary Computation, IEEE Transactions on
Industrial Informatics, and IEEE Transactions on
Cybernetics.
He has filed more than 20 US patents, of which 13 have
been granted. Kwong has a prolific research record. He
has co-authored three research books, eight book
chapters, and over 300 technical papers. According to
Google Scholar, his works have been cited more than
25,000 times with an h-index of 70. In 2014, he was
elevated to IEEE Fellow for his contributions to
optimization techniques in cybernetics and video coding.
He is also a fellow of Asia-Pacific Artificial
Intelligence Association (AAIA) in 2022. Currently, he
serves as the President of the IEEE SMC Society.
Speech Title: Intelligent Video Coding by Data-driven
Techniques and Learning Models
Abstract: In June 6th 2016, Cisco released the White
paper[1], VNI Forecast and Methodology 2015-2020,
reported that 82 percent of Internet traffic will come
from video applications such as video surveillance,
content delivery network, so on by 2020. It also
reported that Internet video surveillance traffic nearly
doubled, Virtual reality traffic quadrupled, TV grew 50
percent and similar increases for other applications in
2015. The annual global traffic will first time exceed
the zettabyte(ZB;1000 exabytes[EB]) threshold in 2016,
and will reach 2.3 ZB by 2020. It implies that 1.886ZB
belongs to video data. Thus, in order to relieve the
burden on video storage, streaming and other video
services, researchers from the video community have
developed a series of video coding standards. Among
them, the most up-to-date is the High Efficiency Video
Coding(HEVC) or H.266 standard, which has successfully
halved the coding bits of its predecessor, H.264/AVC,
without significant increase in perceived distortion.
With the rapid growth of network transmission capacity,
enjoying high definition video applications anytime and
anywhere with mobile display terminals will be a
desirable feature in the near future. Due to the lack of
hardware computing power and limited bandwidth, lower
complexity and higher compression efficiency video
coding scheme are still desired. For higher video
compression performance, the key optimization problems,
mainly decision making and resource allocation problem,
shall be solved. In this talk, I will present the most
recent research and new developments on deep neural
network based video coding and its applications such as
saliency detection, perceptual visual processing and
others. This is very different from the traditional
approaches in video coding. We hope applying these
intelligent techniques to vide coding could allow us to
go further and have more choices in trading off between
cost and resources.
Prof. Xudong Jiang
IEEE Fellow
Nanyang Technological University, Singapore
Xudong Jiang, IEEE Fellow, received the B.Eng. and
M.Eng. from University of Electronic Science and
Technology of China (UESTC), and the Ph.D. degree from
Helmut Schmidt University, Hamburg, Germany. During his
work in UESTC, he received two Science and Technology
Awards from the Ministry for Electronic Industry of
China. From 1998 to 2004, he was with the Institute for
Infocomm Research, A-Star, Singapore, as a Lead
Scientist and the Head of the Biometrics Laboratory,
where he developed a system that achieved the most
efficiency and the second most accuracy at the
International Fingerprint Verification Competition in
2000. He joined Nanyang Technological University (NTU),
Singapore, as a Faculty Member, in 2004, and served as
the Director of the Centre for Information Security from
2005 to 2011. Currently, he is a professor of NTU. Dr
Jiang holds 7 patents and has authored over 200 papers
with over 40 papers in IEEE journals, including 14
papers in IEEE T-IP and 6 papers in IEEE T-PAMI. His
publications are well-cited with H-index 55 and 4 of his
papers were listed as the top 1% highly cited papers in
the academic field of Engineering by Essential Science
Indicators. He served as IFS TC Member of IEEE Signal
Processing Society, Associate Editor for IEEE SPL,
Associate Editor for IEEE T-IP and the founding
editorial board member for IET Biometrics. Currently, Dr
Jiang is an IEEE Fellow and serves as Senior Area Editor
for IEEE T-IP and Editor-in-Chief for IET Biometrics.
His current research interests include image processing,
pattern recognition, computer vision, machine learning,
and biometrics.
Speech Title: Towards Explainable AI: How Deep CNN Solves Problems of ANN
Abstract: Discovering knowledge from data has many applications in various artificial intelligence (AI) systems. Machine learning from the data is a solution to find right information from the high dimensional data. It is thus not a surprise that learning-based approaches emerge in various AI applications. The powerfulness of machine learning was already proven 30 years ago in the boom of neural networks but its successful application to the real world is just in recent years after the deep convolutional neural networks (CNN) have been developed. This is because the machine learning alone can only solve problems in the training data but the system is designed for the unknown data outside of the training set. This gap can be bridged by regularization: human knowledge guidance or interference to the machine learning. This speech will analyze these concepts and ideas from traditional neural networks to the very hot deep CNN neural networks. It will answer the questions why the traditional neural networks fail to solve real world problems even after 30 years’ intensive research and development and how the deep CNN neural networks solve the problems of the traditional neural networks and now are very successful in solving various real world AI problems.
Prof.
Habib Zaidi
IEEE Fellow
University of Geneva, Switzerland
Professor Habib Zaidi is Chief physicist and head of the
PET Instrumentation & Neuroimaging Laboratory at Geneva
University Hospital and faculty member at the medical
school of Geneva University. He is also a Professor at
the University of Groningen (Netherlands) and the
University of Southern Denmark. His research is
supported by the Swiss National Foundation, private
foundations and industry (Total 8.8 M US$) and centres
on hybrid imaging instrumentation (PET/CT and PET/MRI),
computational modelling and radiation dosimetry and deep
learning. He was guest editor for 13 special issues of
peer-reviewed journals and serves on the editorial board
of leading journals in medical physics and medical
imaging. He has been elevated to the grade of fellow of
the IEEE, AIMBE, AAPM, IOMP, AAIA and the BIR. His
academic accomplishments in the area of quantitative PET
imaging have been well recognized by his peers since he
is a recipient of many awards and distinctions among
which the prestigious (100’000$) 2010 kuwait Prize of
Applied sciences (known as the Middle Eastern Nobel
Prize). Prof. Zaidi has been an invited speaker of over
160 keynote lectures and talks at an International
level, has authored over 360 peer-reviewed articles
(h-index=69, >18’000+ citations) in prominent journals
and is the editor of four textbooks.
Speech Title: New Horizons in Deep Learning-assisted Multimodality Medical Image Analysis
Abstract: Positron emission tomography (PET), x-ray computed tomography (CT) and magnetic resonance imaging (MRI) and their combinations (PET/CT and PET/MRI) provide powerful multimodality techniques for in vivo imaging. This talk presents the fundamental principles of multimodality imaging and reviews the major applications of artificial intelligence (AI), in particular deep learning approaches, in multimodality medical imaging. It will inform the audience about a series of advanced development recently carried out at the PET instrumentation & Neuroimaging Lab of Geneva University Hospital and other active research groups. To this end, the applications of deep learning in five generic fields of multimodality medical imaging, including imaging instrumentation design, image denoising (low-dose imaging), image reconstruction quantification and segmentation, radiation dosimetry and computer-aided diagnosis and outcome prediction are discussed. Deep learning algorithms have been widely utilized in various medical image analysis problems owing to the promising results achieved in image reconstruction, segmentation, regression, denoising (low-dose scanning) and radiomics analysis. This talk reflects the tremendous increase in interest in quantitative molecular imaging using deep learning techniques in the past decade to improve image quality and to obtain quantitatively accurate data from dedicated standalone (CT, MRI, SPECT, PET) and combined PET/CT and PET/MRI imaging systems. The deployment of AI-based methods when exposed to a different test dataset requires ensuring that the developed model has sufficient generalizability. This is an important part of quality control measures prior to implementation in the clinic. Novel deep learning techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical medical imaging community. Future opportunities and the challenges facing the adoption of deep learning approaches and their role in molecular imaging research are also addressed.