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Modeling deep structures with application to object detectio
时间:2017-05-21 点击:
报告人:Prof. Wanli Ouyang (欧阳万里 教授)
邀请人:苗启广
报告时间:2017年5月25日10:00
报告地点:主楼四区107会议室
Bibliography:
 

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.
 
Abstract:
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.