We invite Tieniu Tan as the keynote speech of this conference.
Tieniu Tan received his B.Sc. degree in electronic engineering from Xi'an Jiaotong University, China, in 1984, and his MSc and PhD degrees in electronic engineering from Imperial College London, U.K., in 1986 and 1989, respectively.
In October 1989, he joined the Computational Vision Group at the Department of Computer Science, The University of Reading, U.K., where he worked as a Research Fellow, Senior Research Fellow and Lecturer. In January 1998, he returned to China to join the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of the Chinese Academy of Sciences (CAS) as a full professor. He was the Director General of the CAS Institute of Automation from 2000-2007, and the Director of the NLPR from 1998-2013. He is currently Director of the Center for Research on Intelligent Perception and Computing at the Institute of Automation and also serves as Deputy Secretary-General of the CAS and the Director General of the CAS Bureau of International Cooperation. He has published more than 400 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition, and has authored or edited 11 books. He holds more than 70 patents. His current research interests include biometrics, image and video understanding, and information forensics and security.
Dr Tan is a Fellow of the Chinese Academy of Sciences, an International Fellow of the UK Royal Academy of Engineering, and a Fellow of the IEEE and the IAPR (the International Association of Pattern Recognition). He currently serves as President of the IEEE Biometrics Council and Deputy President of the Chinese Association for Artificial Intelligence. He was the Founding Chair of the IAPR Technical Committee on Biometrics, the IAPR/IEEE International Conference on Biometrics (ICB), the IEEE International Workshop on Visual Surveillance, Asian Conference on Pattern Recognition (ACPR) and Chinese Conference on Pattern Recognition (CCPR). He was the Executive Vice President of the Chinese Society of Image and Graphics, Deputy President of the China Computer Federation and the Chinese Automation Association. He has served as chair or program committee member for many major national and international conferences. He is or has served as Associate Editor or member of editorial boards of many leading international journals including IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition, Pattern Recognition Letters, Image and Vision Computing, etc. He is Editor-in-Chief of the International Journal of Automation and Computing. He has given invited talks and keynotes at many universities and international conferences, and has received numerous national and international awards and recognitions.
We invite Jun Wang as the keynote speech of this conference.
Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory. His current research interests include neural networks and their applications. He has been the Editor-in-Chief of the IEEE Transactions on Cybernetics Since 2014 and was an Associate Editor of the journal and its predecessor from 2003 - 2013 and a member of the editorial board of Neural Networks since 2012. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009) and IEEE Transactions on Systems, Man, and Cybernetics - Part C (2002-2005), a member of editorial advisory board of International Journal of Neural Systems (2006-2012), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence.
He was also an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012) and served or is serving in several standing committees of professional associations such as the President of Asia Pacific Neural Network Assembly (2006), IEEE Fellow Committee (2011-2012), Awards Committee (2008, 2012) and Fellow Committee (2010) of IEEE Computational Intelligence Society, Board of Governors of IEEE Systems, Man and Cybernetics Society (2013-2015).
Title:Parallel Data Processing Based on Neurodynamic Optimization in the World of Big Data
In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data processing approaches can expedite the processes and more efficiently deal with big data. In the last few decades, neural computation emerged as a popular area for parallel and distributed data processing. The data processing applications of neural computation included, but not limited to, data sorting, data selection, data mining, data fusion, and data reconciliation. In this talk, neurodynamic approaches to parallel data processing will be introduced, reviewed, and compared. In particular, my talk will compare several mathematical problem formulations of well-known multiple winners-take-all problem and present several recurrent neural networks with reducing model complexity. Finally, the best one with the simplest model complexity and maximum computational efficiency will be highlighted. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance of the continuous-time and discrete-time models. The applications to parallel sorting, rank-order filtering, and data retrieval will be also discussed.
We invite Larry S. Davisas the keynote speech of this conference.
Larry S. Davis received his B.A. from Colgate University in 1970 and his M. S. and Ph. D. in Computer Science from the University of Maryland in 1974 and 1976 respectively. From 1977-1981 he was an Assistant Professor in the Department of Computer Science at the University of Texas, Austin. He returned to the University of Maryland as an Associate Professor in 1981. From 1985-1994 he was the Director of the University of Maryland Institute for Advanced Computer Studies. He was Chair of the Department of Computer Science from 1999-2012. He is currently a Professor in the Institute and the Computer Science Department, as well as Director of the Center for Automation Research. He was named a Fellow of the IEEE in 1997 and of the ACM in 2013.
This talk will cover recent research at the University of Maryland on methods for analyzing the visual content of video to support event recognition in videos and video retrieval based. For the former, I will describe our research on the use of Markov Logic Networks to represent and utilize common sense knowledge to control video event recognition. The second part of the talk will focus on natural language queries to video databases. More than two years ago Google announced that the video upload rate to its sharing site, Youtube, was 60 hours of video every minute. By now, the upload rate is certainly much higher. Searching for videos on Youtube is supported through the text that accompanies the video, user comments, etc., but currently not on the visual or audio content of the video. Our research attempts to link these language queries with visual content. This requires developing so-called zero shot learning algorithms for video classes. That is, users do not provide video search algorithms with examples of videos of interest, just descriptions of the class of interest. I will first describe an approach to video classification based on “clauselets” – small temporal conjunctions of localized activities that are discriminative with respect to classes. Clauselets can be discovered based extensive hand annotations of videos, but a simplification of the model allows us to learn them using zero shot techniques. I will present experimental results on the TRECVID MED13 EKO dataset.