Keynotes:

Abstract: Visual computing, traditionally, is a generic term for all computer science disciplines dealing with images, videos, and other types of visual data. These disciplines mainly include computer graphics, image processing, visualization, computer vision, virtual and augmented reality, and video analytics. This talk shall analyze contemporary visual computing systems from several systematic perspectives. First, contemporary visual data acquisition has shifted from the laboratory in the early days to the fields in recent years with new technical challenges emerging on the visual sensing front. Second, massive visual data acquired for a very diverse range of applications require high-performance computation of visual data via cloud computing. Such extension of visual computing to both ends of the contemporary system now demands pervasive networking to effectively transport such volumetric visual data back and forth. Therefore, the networking of visual data has now become a key component in contemporary visual computing systems which has not been adequately studied before. The investigation of visual computing systems now needs to be significantly deepened to facilitate the researchers to traverse across new domains of exploitation. Several examples of emerging applications with unique design principles will be presented to illustrate the technical challenges we are facing and the potential impacts that contemporary visual computing systems are capable of producing.

Bio: Prof. Changwen Chen is currently Chair Professor of Visual Computing at The Hong Kong Polytechnic University. Before his current position, he served as Dean of the School of Science and Engineering at The Chinese University of Hong Kong, Shenzhen from 2017 to 2020, and concurrently as Deputy Director at Peng Cheng Laboratory from 2018 to 2021. Previously, he has been an Empire Innovation Professor at the State University of New York at Buffalo from 2008 to 2021 and the Allan Henry Endowed Chair Professor at the Florida Institute of Technology from 2003 to 2007. He has served as an Editor-in-Chief for T-MM (2014-2016) and T-CSVT (2006-2009) and has received many professional achievement awards, including the prestigious Alexander von Humboldt Award in 2010, SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2016, and UIUC ECE Distinguished Alumni Award in 2019. He is an IEEE Fellow, an SPIE Fellow, and a Member of the Academia Europaea.

The future of video communication

Baining Guo
Microsoft Research

 

Abstract: I will talk about what the future video communication system might look like, covering both the front-end, the back-end and some fundamental theoretical issues. For the front end, I will talk about 3D immersive video communication. For the back end, I will talk about neural codec, which is on its way to replace our traditional codec. For theoretical issues, I will touch on both the fundamental changes brought by the Transformer in the image and video processing domain and some fundamental questions that we are only begin to ponder.

 

Bio: Dr. Baining Guo is a Distinguished Scientist of Microsoft Corporation. His main research areas are computer graphics, geometric modeling, virtual reality, and computer vision. Prior to joining Microsoft Research in 1999, he was a senior staff researcher with Intel Research in the Silicon Valley. Dr. Guo got his BS degree from Beijing University and his MS and PhD degrees from Cornell University. Dr. Guo is an IEEE fellow and ACM fellow. He is also a member of Canadian Academy of Engineering.

 

 

New frontiers in machine learning interpretability

Mihaela van der Schaar
University of Cambridge

 

Abstract: Medicine has the potential to be transformed by machine learning (ML) by addressing core challenges such as time-series forecasts, clustering (phenotyping), and heterogeneous treatment effect estimation. However, to be embraced by clinicians and patients, ML approaches need to be interpretable. So far though, ML interpretability has been largely confined to explaining the predictions of static classifiers. 

In this keynote, I describe an extensive new framework for ML interpretability. This framework allows us to 1) interpret ML methods for time-series forecasting, clustering (phenotyping), and heterogeneous treatment effect estimation using feature and example-based explanations, 2) provide personalized explanations of ML methods with reference to a set of examples freely selected by the user, and 3) autonomously (re)discover known scientific concepts using concept activation regions, which are generalizations of concept-based explanations.

To learn more about our work in this area - see our website dedicated to this topic - https://www.vanderschaar-lab.com/interpretable-machine-learning/ and our github - https://github.com/vanderschaarlab/Interpretability

 

Bio: https://www.vanderschaar-lab.com/prof-mihaela-van-der-schaar/

 

 

The trend of the post deep learning era: foundation models

Dacheng Tao

JD Explore Academy

 

Bio: Dacheng Tao is the Inaugural Director of the JD Explore Academy and a Senior Vice President of JD.com. He is also an advisor and chief scientist of the digital science institute in the University of Sydney. He mainly applies statistics and mathematics to artificial intelligence and data science, and his research is detailed in one monograph and over 200 publications in prestigious journals and proceedings at leading conferences. He received the 2015 Australian Scopus-Eureka Prize, the 2018 IEEE ICDM Research Contributions Award, and the 2021 IEEE Computer Society McCluskey Technical Achievement Award. He is a fellow of the Australian Academy of Science, the World Academy of Sciences, the Royal Society of NSW, AAAS, ACM, IAPR and IEEE.