Keynote Speakers 主讲嘉宾
Prof. Frederic Dufaux
IEEE Fellow
Université Paris-Saclay, France
Bio: Dr. Frederic Dufaux is a CNRS Research Director at
Université Paris-Saclay, CNRS, CentraleSupélec,
Laboratoire des Signaux et Systèmes (L2S, UMR 8506),
where he is head of the Telecom and Networking
research hub. He is a Fellow of IEEE.
Frederic received the M.Sc. in physics and Ph.D. in
electrical engineering from the Swiss Federal
Institute of Technology (EPFL), Lausanne,
Switzerland, in 1990 and 1994 respectively. He has
over 30 years of experience in research, previously
holding positions at EPFL, Emitall Surveillance,
Genimedia, Compaq, Digital Equipment, and MIT.
Frederic was Vice General Chair of ICIP 2014,
General Chair of MMSP 2018, and Technical Program
co-Chair of ICIP 2019 and ICIP 2021. He is Technical
Program co-Chair of ICIP 2025 and MMSP 2025, and
General Chair of ICME 2026. He served as Chair of
the IEEE SPS Multimedia Signal Processing (MMSP)
Technical Committee in 2018 and 2019. He was a
member of the IEEE SPS Technical Directions Board
from 2018 to 2021. He was Chair of the Steering
Committee of ICME in 2022 and 2023. Since 2025, he
is IEEE SPS Vice President Technical Directions, and
member of the IEEE SPS Board of Governors and
Executive Committee. He was also a founding member
and the Chair of the EURASIP Technical Area
Committee on Visual Information Processing from 2015
to 2021.
He was Editor-in-Chief of Signal Processing: Image
Communication from 2010 until 2019. Since 2021, he
is Specialty Chief Editor of the section on Image
Processing in the journal Frontiers in Signal
Processing.
In 2022, he received the EURASIP Meritorious Service
Award, “for his leadership and contributions for the
development of visual information processing within
EURASIP”.
Frederic is on the Executive Board of Systematic
Paris-Region since 2019, a European competitiveness
cluster which brings together and drives an
ecosystem of excellence in digital technologies and
DeepTech.
He has been involved in the standardization of
digital video and imaging technologies for more than
15 years, participating both in the MPEG and JPEG
committees. He was co-chairman of JPEG 2000 over
wireless (JPWL) and co-chairman of JPSearch. He is
the recipient of two ISO awards for these
contributions.
His research interests include image and video
coding, 3D video, high dynamic range imaging, visual
quality assessment, video surveillance, privacy
protection, image and video analysis, multimedia
content search and retrieval, video transmission
over wireless network. He is author or co-author of
3 books, more than 250 research publications
(h-index=53, 11000+ citations) and more than 25
patents issued or pending. He is in the « World’s
Top 2% Scientists » list from Stanford University.
Prof. Alessandro Foi
IEEE Fellow
Tampere University, Finland
Bio: Alessandro Foi is a Professor of Signal Processing
at Tampere University (TAU), Finland. He leads the
Signal and Image Restoration group and he is the
director of TAU Imaging Research Platform. He is
also the CTO of Noiseless Imaging, a company
specialized in noise-removal, restoration, and
enhancement technology for the imaging industry.
He received the M.Sc. degree in Mathematics from the
Università degli Studi di Milano, Italy, in 2001,
the Ph.D. degree in Mathematics from the Politecnico
di Milano in 2005, and the D.Sc.Tech. degree in
Signal Processing from Tampere University of
Technology, Finland, in 2007. His research interests
include mathematical and statistical methods for
signal processing, functional and harmonic analysis,
and computational modeling of the human visual
system. His work focuses on spatially adaptive
algorithms for the restoration and enhancement of
digital images, on noise modeling for imaging
devices, and on the optimal design of statistical
transformations for the stabilization,
normalization, and analysis of random data. He is a
Fellow of the IEEE for his contributions to image
restoration and noise modeling.
He was the Editor-in-Chief of the IEEE Transactions
on Image Processing from 2021 to 2023. He previously
served as a Senior Area Editor for the IEEE
Transactions on Computational Imaging and as an
Associate Editor for the IEEE Transactions on Image
Processing, the SIAM Journal on Imaging Sciences,
and the IEEE Transactions on Computational Imaging.
He has presented tutorials at several major
international signal processing conferences such as
EUSIPCO (2007), IEEE ICIP (2010, 2014, 2018), and
SPCOM (2020), covering a range of topics including
noise modeling and analysis, adaptive sparse
approximations, image restoration, and inverse
imaging. He is currently a member of the IEEE SPS
Technical Directions Board, of the IEEE TAB/PSPB
Products & Services Committee, and of the IEEE
Conference Publications Committee. He is the Lead
Technical Program Chair of the upcoming IEEE ICIP
2026 in Tampere Finland.
Prof. Dacheng Tao
A Fellow of the Australian Academy of Science,
IEEE Fellow, ACM Fellow
Nanyang Technological University, Singapore
Bio: Dacheng Tao
is currently a Distinguished University Professor
and the Inaugural Director of the Generative AI Lab
in the College of Computing and Data Science at
Nanyang Technological University. He was an
Australian Laureate Fellow and the founding director
of the Sydney AI Centre at the University of Sydney,
the inaugural director of JD Explore Academy and
senior vice president at JD.com, and the chief AI
scientist at UBTECH Robotics. He mainly applies
statistics and mathematics to artificial
intelligence, and his research is detailed in one
monograph and over 300 publications. His
publications have been cited over 140K times and he
has an h-index 180+ in Google Scholar. He received
the 2015 and 2020 Australian Eureka Prize, the 2018
IEEE ICDM Research Contributions Award, 2020
research super star by The Australian, the 2019
Diploma of The Polish Neural Network Society, and
the 2021 IEEE Computer Society McCluskey Technical
Achievement Award. He is a Fellow of the Australian
Academy of Science, ACM and IEEE.
Speech Title: Deep Model Fusion
Abstract: In recent
years, we have witnessed a profound transformation
in the learning paradigm of deep neural networks,
especially in the applications of large language
models and other foundation models. While
conventional deep learning methodologies maintain
their significance, they are now augmented by
emergent model-centric approaches such as
transferring knowledge, editing models, fusing
models, or leveraging unlabeled data to tune models.
Among these advances, deep model fusion techniques
have demonstrated particular efficacy in boosting
model performance, accelerating training, and
mitigating the dependency on annotated datasets.
Nevertheless, substantial challenges persist in the
research and application of effective fusion
methodologies and their scalability to large-scale
foundation models. In this talk, we systematically
present the recent advances in deep model fusion
techniques. We provide a comprehensive taxonomical
framework for categorizing existing model fusion
approaches, and introduce our recent developments,
including (1) weight learning-based model fusion and
data-adaptive MoE upscaling, (2) subspace learning
approaches to model fusion, and (3) enhanced
multi-task model fusion incorporating pre- and
post-finetuning to minimize representation bias
between the merged model and task-specific models.
Prof. Chong-Yung Chi
IEEE Life Fellow, AAIA & AIIA Fellows
National Tsing Hua University, Taiwan, China
Bio: Chong-Yung Chi (IEEE Life Fellow, AAIA & AIIA
Fellows, NAAI Member) received a B.S. degree from
Tatung Institute of Technology, Taipei, Taiwan, in
1975, an M.S. degree from National Taiwan
University, Taipei, Taiwan, in 1977, and a Ph.D.
degree from the University of Southern California,
Los Angeles, CA, USA, in 1983, all in electrical
engineering.
He is a Professor at National Tsing Hua University,
Hsinchu, Taiwan. He has published more than 240
technical papers (with citations more than 8200 by
Google-Scholar), including more than 100 journal
papers (mainly in IEEE TRANSACTIONS ON SIGNAL
PROCESSING), more than 140 peer-reviewed conference
papers, 3 book chapters, and 2 books, including a
textbook, Convex Optimization for Signal
Processing and Communications: From Fundamentals to
Applications, CRC Press, 2017 (which has been
popularly used in a series of invited intensive
short courses at 10 top-ranking universities in
Mainland China since 2010 before its publication).
His research interests include signal processing for
wireless communications, convex analysis and
optimization for blind source separation, biomedical
and hyperspectral image analysis, and currently
focused on Intelligent Fusion of Convex
Optimization and Artificial Intelligence.
Dr. Chi received the 2018 IEEE Signal Processing
Society Best Paper Award, entitled “Outage
Constrained Robust Transmit Optimization for
Multiuser MISO Downlinks: Tractable Approximations
by Conic Optimization,” IEEE Transactions on Signal
Processing, vol. 62, no. 21, Nov. 2014. He has been
a Technical Program Committee member for many
IEEE-sponsored and cosponsored workshops,
symposiums, and conferences on signal processing and
wireless communications, including Co-Organizer and
General Co-Chairman of the 2001 IEEE Workshop on
Signal Processing Advances in Wireless
Communications (SPAWC). He was an Associate Editor
(AE) for four IEEE Journals, including IEEE
TRANSACTIONS ON SIGNAL PROCESSING for 9 years
(5/2001-4/2006, 1/2012-12/2015), and he was a member
of Signal Processing Theory and Methods Technical
Committee (SPTM-TC) (2005-2010), a member of Signal
Processing for Communications and Networking
Technical Committee (SPCOM-TC) (2011-2016), and a
member of Sensor Array and Multichannel Technical
Committee (SAM-TC) (2013-2018), IEEE Signal
Processing Society.
Prof. Badong Chen
Xi 'an Jiaotong University, China
Bio: Badong Chen is a professor at the Institute of
Artificial Intelligence and Robotics, Xi 'an
Jiaotong University, and a Chang Jiang Scholar of
the Ministry of Education. He has graduated from
Tsinghua University with a doctoral degree in
Computer science in 2008. The research fields
include machine learning, artificial intelligence,
brain-computer interfaces, and robotics. More than
300 academic papers have been published in
internationally renowned journals and conferences,
and the papers have been cited more than 17,000
times. More than 30 national invention patents have
been authorized and 6 academic monographs have been
published. He has been selected for the list of the
world's top 2% scientists and the Elsevier China
Highly Cited Researchers List. He has won the First
Prize of Natural Science of the Ministry of
Education, the First Prize of Natural Science of the
Chinese Association of Automation, the Young
Scientist Award of the Chinese Association of
Automation, etc. He has served as a council member
of the Chinese Society for Cognitive Science and an
editorial board member of IEEE Transactions
TNNLS/TCDS/TCSVT. He has presided over the key
projects of the National Natural Science Foundation
of China, the key supported projects of the Major
Research Program of the National Natural Science
Foundation of China, the key projects of the Joint
Fund of the National Natural Science Foundation of
China, the projects of the 973 Program, and the
projects of the National Key Research and
Development Program.