APSIPA U.S. Local Chapter will organize a panel discussion on “Visual Coding for Machines” at 5-6:30pm PST, Match 11, 2023
In recent years, visual data has become the dominating internet traffic. With the emerging 5G and internet of things (IoT) technologies, more and more images and videos are generated by edge devices, sent across networks, and analyzed by machines for tasks such as object detection, instance segmentation, and object tracking. While conventional visual coding techniques aim to preserve the pixel-level fidelity or perceptual quality of decoded images and videos, they are not optimal for machine vision tasks. Therefore, efficient visual coding for machines has become an important topic in academia and industry. This panel brings together experts who are working on related problems to discuss the current research and standardization efforts in the area, and generate ideas for future work.
Panelists:
Lu Yu
Distinguished Professor
Zhejiang University, China
https://person.zju.edu.cn/en/yul
Hari Kalva
Professor and Associate Chair
Department of Electrical Engineering and Computer Science
College of Engineering and Computer Science
Florida Atlantic University, Boca Raton, FL U.S.A.
www.cse.fau.edu/~hari
Shan Liu
Distinguished Scientist and General Manager
Tencent Media Lab, Tencent Video, Tencent Games, U.S.A.
Chair/Co-Chair of MPEG VCM, Co-Editor of VVC/H.266 https://www.linkedin.com/in/shanliu/
Zhibo Chen
Professor, School of Information Science
University of Science and Technology of China, China
https://faculty.ustc.edu.cn/chenzhibo
Fengqing Maggie Zhu
Associate Professor, Elmore Family School of Electrical and Computer Engineering
Purdue University, West Lafayette, U.S.A.
https://engineering.purdue.edu/~zhu0/
Shiqi Wang
Assistant Professor, Department of Computer Science
City University of Hong Kong
https://www.cs.cityu.edu.hk/~shiqwang/
Moderator:
Ying Liu
Assistant Professor, Department of Computer Science & Engineering Santa Clara University, Santa Clara, CA, U.S.A https://www.scu.edu/engineering/faculty/liu-ying/
Meeting Information:
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Meeting ID: 975 5159 2204
Password: 914262
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Meeting ID: 975 5159 2204
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