Keynote Speakers

 

Prof. Weiyao Lin, Shanghai Jiao Tong University, China

林巍峣,教授,上海交通大学

Weiyao Lin received the B.E. degree from Shanghai Jiao Tong University, China, in 2003, the M.E. degree from Shanghai Jiao Tong University, China, in 2005, and the Ph.D degree from the University of Washington, Seattle, USA, in 2010, all in electrical engineering. He is currently a Professor with the Department of Electronic Engineering, Shanghai Jiao Tong University, China. He has authored or coauthored 100+ technical papers on top journals/conferences including TPAMI, IJCV, CVPR, and ICCV. He holds 18 patents and has 10+ under reviewing patents. His research interests include multimedia content understanding, computer vision, video/image compression, and video/image processing applications.

Dr. Lin served as an associate editor for IEEE Trans. Image Processing, IEEE Trans. Circuits & Systems for Video Technology, IEEE Trans. Intelligent Transportation Systems. He is an organizing committee chair of International Conference on Image and Graphics (ICIG) 2017, an area chair/senior PC of AAAI’21, ICPR’20, ACM MM'20, BMVC'19, ICIP'19, and ICME'2018, and an organizer of 6+ workshops in ICCV, ECCV, ACM MM, and ICME. He is a member of a number of international technical committees including IEEE MMSP TC, IEEE MSA TC, and IEEE VSPC TC. He received the Multimedia Rising Star award in ICME'2019, the outstanding Area Chair award in ICME'2018, and the Best Associate Editor award of the month for IEEE Access. He is a senior member of IEEE. Webpage: https://weiyaolin.github.io/

Speech Title: Multi-modality multimedia information analysis and compression

Abstract: With the rapid growth of multimedia applications and services, semantic information, such as objects' motion, action, & property, is of increasing importance in many emerging multimedia applications whose data has become extremely "big". This imposes a huge demand for the efficient extraction and compression of semantic information. In this talk, I will introduce our works on multi-modality multimedia information analysis and compression. Firstly, I will introduce our work on object activity and interaction recognition. We re-model the existing action detection architectures, and develop a long-term parsing & short-term sampling structure. Secondly, we will introduce our work on multi-modality multimedia analysis, which aims to accurately localize and analyze objects based on the joint analysis between audio and video streams. Thirdly I will also present our new work on semantic information compression. We construct a new model to describe the spatial-temporal redundancies in semantic data, and design a new architecture that can compress more than 70% of the semantic data. Finally, I will give some industry application examples of our work.