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美国德州大学圣安东尼奥分校(UTSA)田奇教授学术报告——Large-scale Visual Search
2017/8/3 22:17:07 来源: 作者: cyjb  阅读161次 上传:管理员 组别:管理组
  

报告题目:Large-scale Visual Search(大规模可视化搜索)

报告地点:信科19楼会议室

报告时间:86()上午10:00-11:30

报告摘要

Coupled with the massive social multimedia data and mobile visual search applications, techniques towards large-scale visual search and recognition are emerging. With the development of local invariant visual features and great success in deep learning, recent decade has witnessed the fast advance of large-scale image search. Current state-of-the-art image search algorithms and systems are motivated by the classic bag-of-visual-words model and the scalable index structure, and further powered by the deep learning techniques. Generally, an image search system is involved with several key modules, including feature representation, visual codebook construction, feature quantization, index strategy, and scoring scheme. Besides, post-processing techniques, such as geometric verification, query expansion and multi-modal fusion, can be plugged in to boost the retrieval performance.

In the first part of the talk, I will introduce those related works in each module as mentioned above and discuss the key research problems. In the second part, I will introduce our research work on large scale image search. We have done comprehensive work on feature representation, feature quantization, scalable indexing, spatial verification, et al. Several representative works (i.e., fast geometric verification, a novel co-indexing scheme, and codebook-training-free strategy, recent work in image retrieval with deep learning) will be discussed and the related demos will be shown. In the third part, I will discuss the potential research directions and promising applications on large scale image search.

报告人简介:

Qi Tian is currently a Full Professor in the Department of Computer Science, the University of Texas at San Antonio (UTSA). He was a tenured Associate Professor from 2008-2012 and a tenure-track Assistant Professor from 2002-2008. During 2008-2009, he took one-year Faculty Leave at Microsoft Research Asia (MSRA) as Lead Researcher in the Media Computing Group.

Dr. Tian received his Ph.D. in ECE from University of Illinois at Urbana-Champaign (UIUC) in 2002 and received his B.E. in Electronic Engineering from Tsinghua University in 1992 and M.S. in ECE from Drexel University in 1996, respectively. Dr. Tian’s research interests include multimedia information retrieval, computer vision, pattern recognition and bioinformatics and published over 390 refereed journal and conference papers (including 90+ IEEE/ACM Transactions papers and 75 CCF Category A conference papers). He was the co-author of a Best Paper in ACM ICMR 2015, a Best Paper in PCM 2013, a Best Paper in MMM 2013, a Best Paper in ACM ICIMCS 2012, a Top 10% Paper Award in MMSP 2011, a Best Student Paper in ICASSP 2006, and co-author of a Best Student Paper Candidate in ICME 2015, and a Best Paper Candidate in PCM 2007.

Dr. Tian research projects are funded by ARO, NSF, DHS, Google, FXPAL, NEC, SALSI, CIAS, Akiira Media Systems, HP, Blippar and UTSA. He received 2017 UTSA President’s Distinguished Award for Research Achievement, 2016 UTSA Innovation Award, 2014 Research Achievement Awards from College of Science, UTSA, 2010 Google Faculty Award, and 2010 ACM Service Award. He is the associate editor of IEEE Transactions on Multimedia (TMM), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Multimedia System Journal (MMSJ), and in the Editorial Board of Journal of Multimedia (JMM) and Journal of Machine Vision and Applications (MVA). Dr. Tian is the Guest Editor of IEEE Transactions on Multimedia, Journal of Computer Vision and Image Understanding, etc.

Dr. Tian is a Fellow of IEEE. 田奇教授被评为2016年多媒体领域最有影响力的Top 10学者之一(by Aminer.org)。 田奇教授也是教育部长江讲座教授和中科院海外评审专家。

欢迎全校感兴趣的师生参加!

计算智能重庆市重点实验室

重庆邮电大学计算机学院

2017年8