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Modeling deep structures with application to object detection and pose estimation
时间:2017-05-21 11:18    点击:   所属单位:计算机学院
讲座名称 Modeling deep structures with application to object detection and pose estimation
讲座时间 2017-05-25 10:00:00
讲座地点 主楼四区107会议室
讲座人 欧阳万里
讲座人介绍
Wanli Ouyang received the PhD degree in the Department of Electronic Engineering, The Chinese University of Hong Kong, where he is now a research assistant professor. He will be the senior lecturer in the University of Sydney this June. His research interests include image processing, computer vision and pattern recognition. He is the first author of 6 papers on TPAMI and IJCV, and has published more than 30 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.
 
讲座内容 Deep learning attempts to learn feature representation by multiple levels of abstraction. 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 using deep learning as a tool for 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.
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