Wed, 25 May, 08:00 - 09:30 France Time (UTC +2)
Wed, 25 May, 06:00 - 07:30 UTC
Wed, 25 May, 02:00 - 03:30 New York Time (UTC -4)
Moderator: C.-C. Jay Kuo, University of Southern California, USA
- Xilin Chen, Chinese Academy of Science, China
- Weisi Lin, Nanyang Technological University, Singapore
- Shan Liu, Tencent America, USA
- Yi Ma, University of California at Berkeley, USA
- Helen Meng, Chinese University of Hong Kong, Hong Kong
Machine learning has played an increasingly important role in modern signal processing. It has been widely used to solve multimedia problems, including audio, speech, image and video, graphics, 3D point clouds, etc. The data-driven methodology is expected to continue to grow. Many powerful data-driven solutions are based on deep learning. We have seen the impact of deep learning in numerous conferences and journals. There is however a huge gap between deep learning and classic signal processing disciplines. The former is a black box with a large model size. It is computationally expensive and data hungry. The latter is a white box with a smaller model size. It is computationally effective and can be easily adapted to smaller datasets. It would be desired to find a way to bridge the two. In this panel, we invite world leading experts to express their opinions on a couple of key questions: Is there a role for classical signal processing to play in the machine learning era? Will there be some non-deep-learning-based alternatives in machine learning? etc.
- Big data and machine learning have attracted a lot of attention in the last decade. Through the construction of large datasets, many difficult problems in various domains such as natural language processing (NLP), computer vision (CV), and computer graphics (CG) can be greatly simplified. Do you see any problem in proceeding along this direction? What are the limitations of this data-driven methodology?
- Deep learning is the dominating tool in machine learning, which is widely used in acoustics, signal, speech, and multimedia processing nowadays. Some junior researchers may have doubts on the value of classical signal processing training (e.g., linear algebra, probabilities, etc.). In your opinion, is classical signal processing still valuable? How can they contribute to modern machine learning? Is it possible to find learning-based substitutes without following the deep learning paradigm?
- What are future R&D opportunities and/or directions in the interplay of machine learning, signal processing and multimedia computing? Some concrete examples are helpful.
- What advice will you offer to junior students, researchers, and engineers with major in signal processing and multimedia so that they can be better prepared for the job market and/or an academic career?
C.-C. Jay Kuo is the holder of the William M. Hogue Professorship in Electrical and Computer Engineering, a Distinguished Professor of Electrical and Computer Engineering and Computer Science, and the Director of the USC Multimedia Communication Laboratory (MCL) at the University of Southern California. His research activities lie in multimedia and green computing. He has received several awards for his research contributions, including the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 2020 IEEE TCMC Impact Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo has guided 161 students to their PhD degrees and supervised 31 postdoctoral research fellows. He is listed as the top advisor in the Mathematics Genealogy Project in terms of the number of supervised PhD students. He is the recipient of the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of NAI, AAAS, IEEE and SPIE.
Xilin Chen is currently a Professor with the Institute of Computing Technology, Chinese Academy of Sciences (CAS). He has authored one book and more than 300 papers in refereed journals and proceedings in the areas of computer vision, pattern recognition, image processing, and multimodal interfaces. He is a fellow of ACM, IAPR, IEEE and CCF. He was a recipient of several awards, including China’s State Natural Science Award in 2015, and China’s State S&T Progress Award in 2000, 2003, 2005, and 2012. He served as an Organizing Committee member for many conferences, including the General Co-Chairs for FG13/FG18 / VCIP 2022. He is/was the Area Chair of CVPR, ICCV, and ECCV. He is / was an Associate Editor of the IEEE Transactions on Image Processing, the IEEE Transactions on Multimedia, and Senior Associate Editor of Journal of Visual Communication and Image Representation, a Leading Editor of the Journal of Computer Science and Technology, and an Associate Editor-in-Chief of the Chinese Journal of Computers, and Chinese Journal of Pattern Recognition and Artificial Intelligence.
Weisi Lin received the bachelor’s degree in electronics and the master’s degree in digital signal processing from Sun Yat-sen University, Guangzhou, China, and the Ph.D. degree in computer vision from King’s College London, U.K. He is currently a Professor with the School of Computer Science and Engineering, Nanyang Technological University, Singapore. His research interests include image processing, perceptual modeling, video compression, multimedia communication, and computer vision. He is a fellow of IEEE and IET, an honorary fellow of the Singapore Institute of Engineering Technologists, and a Chartered Engineer in U.K. He was the Chair of the IEEE MMTC Special Interest Group on Quality of Experience. He has served as a Lead Guest Editor for a Special Issue on Perceptual Signal Processing for the IEEE Journal of Selected Topics in Signal Processing in 2012. He has also served or serves as an Associate Editor for IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Multimedia, IEEE Signal Processing Letters, and Journal of Visual Communication and Image Representation. He was awarded as the Distinguished Lecturer for IEEE Circuits and Systems Society in 2016–2017. He has been awarded Highly Cited Researcher 2019, 2020 and 2021 by Clarivate Analytics.
Shan Liu received the B.Eng. degree in electronic engineering from Tsinghua University and the M.S. and Ph.D. degrees in electrical engineering from the University of Southern California. She is currently a Tencent Distinguished Scientist, General Manager of Tencent Media Lab and General Manager, Platform Technologies of Tencent Online Video.. She was formerly Director of Media Technology Division at MediaTek USA. She was also formerly with MERL and Sony. She has been an active contributor to international standards for more than a decade and has numerous technical proposals adopted into various standards, such as VVC, HEVC, OMAF, DASH, MMT, and PCC. She holds more than 400 granted U.S. patents. She served an Editor of H.265/HEVC SCC and H.266/VVC standards. She received the Best AE Award from IEEE Transactions on Circuits and Systems for Video Technology in 2019 and 2020. She has been the Vice Chair of IEEE Data Compression Standards Committee since 2019. She was named the APSIPA Distinguished Industry Leader in 2018. Dr. Liu is a Fellow of IEEE.
Yi Ma is a Professor at the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research interests include computer vision, high-dimensional data analysis, and intelligent systems. Yi received his Bachelor’s degrees in Automation and Applied Mathematics from Tsinghua University in 1995, two Masters degrees in EECS and Mathematics in 1997, and a PhD degree in EECS from UC Berkeley in 2000. He has been on the faculty of UIUC ECE from 2000 to 2011, the principal researcher and manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Executive Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He then joined the faculty of UC Berkeley EECS in 2018. He has published about 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized principal component analysis, and high-dimensional data analysis. He received the NSF Career award in 2004 and the ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision from ICCV 1999 and best paper awards from ECCV 2004 and ACCV 2009. He has served as the Program Chair for ICCV 2013 and the General Chair for ICCV 2015. He is a Fellow of IEEE, ACM, and SIAM.
Helen Meng received the B.S., M.S., and Ph.D. degrees in electrical engineering from the Massachusetts Institute of Technology. She is currently Chair Professor of the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong. In 2019, her inter-disciplinary research team was awarded the first HKSAR Government RGC Theme-based Research Project on Artificial Intelligence. In 2020, she helped establish the CUHK-led Centre for Perceptual and Interactive Intelligence at the Hong Kong Science & Technology Park. She is Chair of Curriculum Development in the CUHK-JC AI4Future Project, which has developed and published (in 2021) the first comprehensive pre-tertiary AI education curriculum that is being taught across Hong Kong. She was former Department Chairman and Associate Dean of Research with CUHK Faculty of Engineering. Her research interests include human–computer interaction via multimodal and multilingual spoken language systems, and spoken language processing to support learning, digital health and wellbeing. She was former Editor-in-Chief (2009-2011) of the IEEE Transactions on Audio, Speech and Language Processing and recipient of the 2019 IEEE Signal Processing Society Leo L. Beranek Meritorious Service Award. She has served in the International Speech Communication Association (ISCA) Board and International Advisory Council. Prof. Helen Meng is a Fellow of IEEE and ISCA.