摘要：Structure in data provides rich information that helps to reduce the complexity and improves the effectiveness of a model. In this talk, an introduction will be given on the recent progress in using deep learning as a tool for modeling the structure in visual data. We show that observation in our problem are useful in modeling the structure of deep model and help to improve the effectiveness of deep models for many computer vision problems.
报告人简介：Wanli Ouyang received the PhD degree in the Department of Electronic Engineering, The Chinese University of Hong Kong. He is now a senior lecturer at the University of Sydney. His research interests include image processing, computer vision and pattern recognition. He is the first author of 7 papers on TPAMI and IJCV, and has published around 50 papers on top tier conferences like CVPR, ICCV and NIPS. ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most important grand challenges in computer vision. The team led by him ranks No. 1 in the ILSVRC 2015 and ILSVRC 2016. He receives the best reviewer award of ICCV. He has been the reviewer of many top journals and conferences such as IEEE TPAMI, TIP, IJCV, SIGGRAPH, CVPR, and ICCV. He is a senior member of the IEEE.