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20180815-25 杨巍:Human pose estimation with deep learning

2018-8-10 00:36| 发布者: 程一-计算所| 查看: 244| 评论: 0

摘要: 报告嘉宾:杨巍(香港中文大学)报告时间:2018年08月15日(星期三)晚上20:00(北京时间)报告题目:Human pose estimation with deep learning主持人:欧阳万里(香港中文大学)报告人简介:Wei Yang is currently ...

报告嘉宾:杨巍香港中文大学

报告时间:2018年08月15日(星期三)晚上20:00(北京时间)

报告题目:Human pose estimation with deep learning

主持人:欧阳万里(香港中文大学


报告人简介:

Wei Yang is currently a final year Ph.D. student at the Electronic Engineering, the Chinese University of Hong Kong, advised by Prof. Xiaogang Wang. He also works with the Multimedia Laboratory, CUHK. He was a visiting student at CMU working on visual navigation with Prof. Abhinav Gupta from November 2017 to April 2018. He achieved the master's degree in Computer Science under the supervision of Prof. Liang Lin at Sun Yat-sen University. He achieved my BEng in Software Engineering, Sun Yat-sen University. His research interests include computer vision, deep learning, and deep reinforcement learning. 


个人主页:

http://www.ee.cuhk.edu.hk/~wyang/


报告摘要:

Articulated human pose estimation is a fundamental yet challenging task in computer vision. The goal is to estimate 2D or 3D locations of human body joints given an image or a video sequence. It serves as informative knowledge for higher-level applications, such as activity recognition, human-computer interaction, robotics vision, and autonomous driving. Although promising progress has been achieved by deep neural networks, obtaining accurate human pose estimation is still nontrivial due to the highly articulated human body limbs, occlusion, cluttered background, scale variation, and foreshortening. On the other hand, 3D human pose estimation for RGB images in the wild is still a challenge due to the limitation of annotated data. In this talk, I will present our recent research work to tackle these challenges with deep learning methods. pose estimation with deep learning method enhanced by incorporating these three key ingredients.


参考文献:

[1] End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation. Wei Yang, Wanli Ouyang, Hongsheng Li, and Xiaogang Wang. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[2] Multi-Context Attention for Human Pose Estimation. Xiao Chu, Wei Yang, Wanli Ouyang, Cheng Ma, Alan L. Yuille, Xiaogang Wang。 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[3] Learning Feature Pyramids for Human Pose Estimation. Wei Yang, Shuang Li, Wanli Ouyang, Hongsheng Li, Xiaogang Wang. International Conference on Computer Vision (ICCV), 2017.

[4] 3D Human Pose Estimation in the Wild by Adversarial Learning. Wei Yang, Wanli Ouyang, Xiaolong Wang, Jimmy Ren, Hongsheng Li, Xiaogang Wang. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.


18-25期VALSE在线学术报告参与方式:


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特别鸣谢本次Webinar主要组织者:

VOOC责任委员:欧阳万里(香港中文大学



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