Keynote Speaker: Kyoung Mu Lee
Talk Title:
Toward Practical Image Super-Resolution and Generalized Image Transformation
Talk Abstract:
Image Super-Resolution (SR), one of the important computer vision technologies for upscaling Low-Resolution (LR) images to High-Resolution (HR), is gaining significance with the proliferation of online broadcasting, streaming services, and social media platforms. However, current SR methods fall short in real-world applications—they often lack adaptability to various image degradations and constraints on output shapes and dimensions. In this talk, we aim to address these shortcomings by introducing practical strategies to diversify the application of SR to accommodate any input and output specifications. To handle the array of degradation encountered in real LR images, we present a novel GAN-based technique, Adaptive Data Loss (ADL), which facilitates the unsupervised learning of degradation types. Furthermore, we introduce SRWarp, an innovative SR model capable of generating images of any desired shape. We have integrated both ADL and SRWarp into a cohesive framework, SelfWarp, which is adept at executing complex image transformation in real-world SR without the need for matched training pairs. Our empirical results showcase the ability of our novel SR algorithms to enhance real-world imaging tasks effectively.
BIO:
Kyoung Mu Lee (Fellow, IEEE) is currently the Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI); He is the director of the Interdisciplinary Graduate Program in Artificial Intelligence at Seoul National University (SNU). He is an Advisory Board Member of the Computer Vision Foundation (CVF). He was a Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (APSIPA), from 2012 to 2013. He has received several awards, in particular, the Medal of Merit and the Scientist of Engineers of the Month Award from the Korean Government, in 2018 and 2020, respectively; the Most Influential Paper Over the Decade Award by the IAPR Machine Vision Application, in 2009; the ACCV Honorable Mention Award, in 2007; and the SNU Excellence in Research Award in 2020. He has also served as a General Chair for ICCV2019, ACMMM2018, and ACCV2018; a Program Chair for ACCV2012; a Track Chair for ICPR2020 and ICPR2012; and an Area Chair for CVPR, ICCV, and ECCV many times. He has served as an Associate Editor-in-Chief (AEIC) and an Associate Editor for the Machine Vision and Application (MVA) journal, the IPSJ Transactions on Computer Vision and Applications (CVA), and the IEEE SIGNAL PROCESSING LETTERS (SPL); and an Area Editor for the Computer Vision and Image Understanding (CVIU). He is the founding member and served as the President of the Korean Computer Vision Society (KCVS). Prof. Lee is a Fellow of IEEE, a member of the Korean Academy of Science and Technology (KAST) and the National Academy of Engineering of Korea (NAEK).
Talk Title:
Toward Practical Image Super-Resolution and Generalized Image Transformation
Talk Abstract:
Image Super-Resolution (SR), one of the important computer vision technologies for upscaling Low-Resolution (LR) images to High-Resolution (HR), is gaining significance with the proliferation of online broadcasting, streaming services, and social media platforms. However, current SR methods fall short in real-world applications—they often lack adaptability to various image degradations and constraints on output shapes and dimensions. In this talk, we aim to address these shortcomings by introducing practical strategies to diversify the application of SR to accommodate any input and output specifications. To handle the array of degradation encountered in real LR images, we present a novel GAN-based technique, Adaptive Data Loss (ADL), which facilitates the unsupervised learning of degradation types. Furthermore, we introduce SRWarp, an innovative SR model capable of generating images of any desired shape. We have integrated both ADL and SRWarp into a cohesive framework, SelfWarp, which is adept at executing complex image transformation in real-world SR without the need for matched training pairs. Our empirical results showcase the ability of our novel SR algorithms to enhance real-world imaging tasks effectively.
BIO:
Kyoung Mu Lee (Fellow, IEEE) is currently the Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI); He is the director of the Interdisciplinary Graduate Program in Artificial Intelligence at Seoul National University (SNU). He is an Advisory Board Member of the Computer Vision Foundation (CVF). He was a Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (APSIPA), from 2012 to 2013. He has received several awards, in particular, the Medal of Merit and the Scientist of Engineers of the Month Award from the Korean Government, in 2018 and 2020, respectively; the Most Influential Paper Over the Decade Award by the IAPR Machine Vision Application, in 2009; the ACCV Honorable Mention Award, in 2007; and the SNU Excellence in Research Award in 2020. He has also served as a General Chair for ICCV2019, ACMMM2018, and ACCV2018; a Program Chair for ACCV2012; a Track Chair for ICPR2020 and ICPR2012; and an Area Chair for CVPR, ICCV, and ECCV many times. He has served as an Associate Editor-in-Chief (AEIC) and an Associate Editor for the Machine Vision and Application (MVA) journal, the IPSJ Transactions on Computer Vision and Applications (CVA), and the IEEE SIGNAL PROCESSING LETTERS (SPL); and an Area Editor for the Computer Vision and Image Understanding (CVIU). He is the founding member and served as the President of the Korean Computer Vision Society (KCVS). Prof. Lee is a Fellow of IEEE, a member of the Korean Academy of Science and Technology (KAST) and the National Academy of Engineering of Korea (NAEK).
Keynote Speaker: Chang Wen Chen
Talk Title:
New Paradigm in Visual Computing: Evolution from Algorithms to Systems with New Technical Challenges
Talk 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 the front-end and the back-end 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 the new paradigm of contemporary visual computing systems which has not been adequately studied before. The investigation of visual computing systems now needs to be critically 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:
Chang Wen 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 (SUNY) 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 IEEE Trans. Multimedia (2014-2016) and IEEE Trans. Circuits and Systems for Video Technology (2006-2009). He has received many professional achievement awards, including ten (10) Best Paper Awards in premier publication venues, the prestigious Alexander von Humboldt Award in 2010, the 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 (2005), an SPIE Fellow (2007), and a Member of the Academia Europaea (2021).
Talk Title:
New Paradigm in Visual Computing: Evolution from Algorithms to Systems with New Technical Challenges
Talk 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 the front-end and the back-end 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 the new paradigm of contemporary visual computing systems which has not been adequately studied before. The investigation of visual computing systems now needs to be critically 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:
Chang Wen 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 (SUNY) 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 IEEE Trans. Multimedia (2014-2016) and IEEE Trans. Circuits and Systems for Video Technology (2006-2009). He has received many professional achievement awards, including ten (10) Best Paper Awards in premier publication venues, the prestigious Alexander von Humboldt Award in 2010, the 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 (2005), an SPIE Fellow (2007), and a Member of the Academia Europaea (2021).
Keynote Speaker: Ko Nishino
Talk Title:
Looking at people and roads
Talk Abstract: My lab's research goal is to elevate computer vision into a truly intelligent perceptual modality for computers. Towards this goal, our research centers on three thrusts:human modeling and behavior analysis; appearance modeling, inverse rendering, and material recognition; and physics-based vision and computational photography. Our research finds applications in AR/VR, graphics, HCI, and robotics, and our current focus is autonomous driving, driving assistance, elderly care, and assisted living. In this talk, I will discuss our recent works on using passive observations from static cameras to detect and estimate human behaviors that reflect their internal states. I will also introduce our works on what we can tell about the road from a car-mounted camera.
BIO:
Ko Nishino received the BE and ME degrees in information and communication engineering from the University of Tokyo, Tokyo, Japan, in 1997 and 1999, respectively, and the PhD degree in computer science from the University of Tokyo, Tokyo, Japan, in 2002. He is currently a professor at the Department of Intelligence Science and Technology, Kyoto University. Before joining Kyoto University in 2018, he was a professor with the Department of Computer Science, Drexel University. His primary research interests lie in computer vision and machine learning, including appearance modeling and material recognition, human behavior analysis, and computational photography. He received the NSF CAREER Award, in 2008.
Talk Title:
Looking at people and roads
Talk Abstract: My lab's research goal is to elevate computer vision into a truly intelligent perceptual modality for computers. Towards this goal, our research centers on three thrusts:human modeling and behavior analysis; appearance modeling, inverse rendering, and material recognition; and physics-based vision and computational photography. Our research finds applications in AR/VR, graphics, HCI, and robotics, and our current focus is autonomous driving, driving assistance, elderly care, and assisted living. In this talk, I will discuss our recent works on using passive observations from static cameras to detect and estimate human behaviors that reflect their internal states. I will also introduce our works on what we can tell about the road from a car-mounted camera.
BIO:
Ko Nishino received the BE and ME degrees in information and communication engineering from the University of Tokyo, Tokyo, Japan, in 1997 and 1999, respectively, and the PhD degree in computer science from the University of Tokyo, Tokyo, Japan, in 2002. He is currently a professor at the Department of Intelligence Science and Technology, Kyoto University. Before joining Kyoto University in 2018, he was a professor with the Department of Computer Science, Drexel University. His primary research interests lie in computer vision and machine learning, including appearance modeling and material recognition, human behavior analysis, and computational photography. He received the NSF CAREER Award, in 2008.