报告题目：Multi-Objective Convolutionl Learning for Face Labeling 

报告时间：3月 25号下午13:30 

报告地点：三号学科楼S410 

报告人：Ming-Hsuan Yang教授 

主持人：刘青山院长 

欢迎广大师生踊跃参加！ 

报告简介: In this work, we formulate face labeling as a conditional random field with unary and pairwise classifiers. We develop a multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Furthermore, we regularize the network by using a nonparametric prior as a new input channel in addition to the input RGB image, and show that significant performance improvements can be achieved with a much smaller network size. Experiments on both the LFW and Helen datasets demonstrate state-of-the-art results of the proposed algorithm. 

报告人简介: Ming-Hsuan Yang is an associate professor in Electrical Engineering and Computer Science at University of California, Merced. He received the PhD degree in Computer Science from the University of Illinois at Urbana-Champaign in 2000. He serves as an area chair for several conferences including IEEE Conference on Computer Vision and Pattern Recognition, IEEE International Conference on Computer Vision, European Conference on Computer Vision, Asian Conference on Computer, and AAAI National Conference on Artificial Intelligence. He serves as a program co-chair for Asian Conference on Computer Vision in 2014 and general co-chair in 2016. He serves as an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence (2007 to 2011), International Journal of Computer Vision, Image and Vision Computing and Journal of Artificial Intelligence Research. Yang received the Google Faculty Award in 2009, and the Faculty Early Career Development (CAREER) award from the National Science Foundation in 2012. 

信息与控制学院 
大数据分析技术重点实验室 
江苏省气象能源利用与控制工程技术研究中心 
“大气环境与装备技术”协同创新中心 
2015-03-24