报告题目： Big Media Data: Search and Management
报告人: 王井东，微软亚洲研究院，Lead Researcher
The explosion of images, videos and other media data in the Internet, mobile devices, and desktops has attracted more and more interest in the Big Media research area. Big media opens great unprecedented opportunities to address many challenging computing problems. In this talk, I will give a summary of our works on big media data search and management. In particular, I will present our recent works on compact coding for large scale similarity search: composite quantization for approximate nearest neighbor search (ICML 2014) and sparse composite quantization (CVPR 2015).
Jingdong Wang is a Lead Researcher at the Visual Computing Group, Microsoft Research Asia. He received the M.Eng. and B.Eng. degrees in Automation from the Department of Automation, Tsinghua University, Beijing, China, in 2001 and 2004, respectively, and the PhD degree in Computer Science from the Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Hong Kong, in 2007. His areas of interest include computer vision, machine learning, pattern recognition, and multimedia computing. At present, he is mainly working on the Big Media project, including large-scale indexing and clustering, Web image search and mining. He has published 100+ papers, including one single-authored book, book chapters, and other papers in top conferences and prestigious international journals such as CVPR, ICCV, ACMMM, ICML, SIGIR, TPAMI, IJCV, TIP, TOG (Siggraph), and so on.
He has been served as an area chair in ACMMM 2015, a track chair in ICME 2012, a special session chair in ICMR 2014, an area chair in ICME 2014, a program committee member or a reviewer in top conferences and journals, including CVPR, ICCV, NIPS, SIGIR, SIGGRAPH, and ACMMM, TPAMI, IJCV, TIP, TKDE, TMM, ToMM. He has also been invited to serve as an editorial board member in the international journal of multimedia tools and applications, an associate editor of the international journal of Neurocomputing.