报告题目：Modeling deep structures with application to object detection and pose estimation
时 间：2017年4月12日上午10:30-12:00
地 点：计算所446会议室
报告摘要：
Deep learning is a hot area of machine learning that attempts to learn in multiple levels of representation. It is found to be useful in speech recognition, face recognition, image classification, biology, physics, and material science. In this talk, a brief introduction will be given on our recent progress in modeling the structure in visual data for object detection and human pose estimation. We show that observation in our problem are useful in modeling the structure of deep model and help to improve the performance of deep models for our problem.
报告人简介：
WanliOuyang received the PhD degree in the Department of Electronic Engineering, The Chinese University of Hong Kong, where he is now a research assistant professor. His research interests include image processing, computer vision and pattern recognition. He is the first/correspondence author of 6 papers on TPAMI and IJCV, and has published 26 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, TSP, TITS, TNN, CVPR, and ICCV. He is a senior member of the IEEE.
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