报告嘉宾：徐畅（University of Technology, Sydney）
报告题目： Multi-view Learning [Slides]
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. For example, a person can be identified by face, fingerprint, signature or iris with information obtained from multiple sources, while an image can be represented by its color or texture features, which can be seen as different feature subsets of the image. In this talk, we will organize the similarities and differences between the varieties of multi-view learning approaches, highlight their limitations, and then demonstrate the basic fundamentals for the success of multi-view learning. The thorough investigation on the view insufficiency problem and the in-depth analysis on the influence of view properties will be beneficial for the continuous development of multi-view learning.
 Chang Xu, Dacheng Tao, Chao Xu, “A Survey on Multi-view Learning”, arXiv, 2013.
 Chang Xu, Dacheng Tao, Chao Xu, “Large-margin Multi-view Information Bottleneck”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2014.
 Chang Xu, Dacheng Tao, Chao Xu, “Multi-view Intact Space Learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2015.
Chang Xu is a Ph.D. student at Peking University. Previously, he was a research assistant at Centre for Quantum Computation and Intelligent Systems (QCIS), University of Technology, Sydney (UTS), under the supervision of Prof. Dacheng Tao. His research interests lie primarily in machine learning, multimedia search and computer vision.
报告嘉宾：薛天帆（MIT CS Dept）
报告题目：A Computational Approach for Obstruction-Free Photography [Slides]
We present a unified computational approach for taking photos through reflecting or occluding elements such as windows and fences. Rather than capturing a single image, we instruct the user to take a short image sequence while slightly moving the camera. Differences that often exist in the relative position of the background and the obstructing elements from the camera allow us to separate them based on their motions, and to recover the desired background scene as if the visual obstructions were not there. We show results on controlled experiments and many real and practical scenarios, including shooting through reflections, fences, and raindrop-covered windows.
 Tianfan Xue, Michael Rubinstein, Ce Liu, William T. Freeman, A Computational Approach for Obstruction-Free Photography, ACM SIGGRAPH 2015.
Tianfan Xue is currently a fourth-year Ph.D. student in MIT CSAIL, working with William T. Freeman. Before that, he received his B.E. degree from Tsinghua Universtiy, and M.Phil. degree from The Chinese University of Hong Kong. His research interests include computer vision, image processing, and machine learning.