报告题目：Dictionary Separation in Sparse Representation [Slides]
 Shenghua Gao, IvorWai-Hung Tsung, Yi Ma. Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization. IEEE Transactions on Image Processing (TIP),23(2):623 – 634, Feb 2014.
 Shenghua Gao, Kui Jia, Liansheng Zhuang, Yi Ma, “Neither global nor local: regularized patch-based representation for single sample face recognition”, International Journal of Computer Vision (IJCV), Volume 111 Issue 3, Pages 365-383, February 2015
报告摘要： Decomposing the dictionary into some sub-dictionaries with different properties would greatly boost the performance of sparse representation many computer vision tasks. In this report, I will present two algorithms for multi-dictionaries based sparse representation as well as their applications, including fine-grained image categorization and one-shot face recognition.
报告人简介： Shenghua Gao is an assistant professor in ShanghaiTech University, China. He received the B.E. degree from the University of Science and Technology of China in 2008 (outstanding graduates), and received the Ph.D. degree from the Nanyang Technological University in 2012. From Jun 2012 to Aug 2014, he worked as a research scientist in Advanced Digital Sciences Center, Singapore. From Jan 2015 to June 2015, he visited UC Berkeley as a visiting scholar. His research interests include computer vision and machine learning. He has published more than 30 papers on object and face recognition related topics in many international conferences and journals, including IEEE T-PAMI,IJCV, IEEE TIP, IEEE TNNLS, IEEE TMM, IEEE TCSVT, CVPR, ECCV, etc. He was awarded the Microsoft Research Fellowship in 2010.
报告嘉宾2：付彦伟（Disney research pittsburgh）
报告题目：Transductive Multi-View Zero-Shot Learning [Slides]
 Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation, ECCV 2014, Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Zhenyong Fu and Shaogang Gong;
 Transductive multi-view zero-shot learning, IEEE TPAMI to appear, Yanwei Fu, Timothy M. Hospedales, Tao Xiang, and Shaogang Gong
报告摘要： Attribute learning is emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in image object recognition and relatively simple human action classification. However, attributes are very limited in understanding more complex image/video classes. In this talk, I will introduce our approaches for generalising the previous attribute learning framework to transductive multi-view semantic embedding for zero-shot learning. Specifically, we identify and solve three challenging problems in canonical zero-shot learning pipeline, i.e. projection domain shift problem, prototype sparsity problem and multiple semantic representations embedding problem. Our framework greatly improves the zero-shot learning accuracy on several benchmark dataset.
报告人简介： Yanwei Fu received the PhD degree from Queen Mary University of London in 2014, and the MEng degree from the Department of Computer Science & Technology, Nanjing University in 2011, China. He is a postdoctoral researcher with Leonid Sigal in Disney Research, Pittsburgh, which is co-located with Carnegie Mellon University. His research interests include image and video understanding and description, robust ranking and learning to rank, and large-scale surveillance video analysis.