Panels

Synopsis

With more and more data challenges such as ImageNet and ActivityNet organized in leading conferences and workshops, it becomes popular to evaluate the performance of algorithms in benchmark datasets. Such challenges are becoming increasingly popular on academic research. Should challenges and competitions on public datasets be the primary driver of multimedia research? Does high quality research necessarily correspond to high ranks in challenges, and vice versa? This panel will discuss the both the positive and negative influences of data challenges on academic research and research community.

Moderator

Prof. Junsong Yuan, State University of New York, Buffalo, USA

Panelists

Mohan Kankanhalli, National University of Singapore
Wenjun Zeng, Microsoft Research Asia, China
Xilin Chen, Chinese Academy of Science, China
Tao Mei, JD Research, China
Zhou Ren, Snap, USA

Synopsis

Multimedia technology is undergoing a vigorous development and revolution, fueled by the success of deep learning algorithms. With rapid innovation in software and hardware to build deep learning models, however, organizations face the challenge to select the right tools that will enable them to leverage AI in enterprise applications. This drives the business need for a common process and open standard to simplify the operational deployment and integration of machine learning algorithms. This panel will invite several leading senior scientists in Multimedia and focus on discussing the topic received increasingly attention, i.e., the challenges and opportunities in the commercialization of multimedia Technologies.

Moderator

Dr. Liang Lin, SenseTime Group Ltd., China, Sun Yat-sen University, China

Panelists

Xiaodan Liang, Carnegie Mellon University, USA
Zhu Li, University of Missouri, USA
Fatih Porikli, Huawei, USA, Australia National University, Australia
Lei Zhang, Microsoft Research, USA
Wen-Huang Cheng, Academia Sincia, Taiwan

Panel Chairs

Jiebo Luo, U. of Rochester, USA
Qi Tian, UT San Antonio, USA