图像所系列学术报告：Toward Understanding Human Actions in Unconstrained Environment-上海交通大学电子信息与电气工程学院

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图像所系列学术报告：Toward Understanding Human Actions in Unconstrained Environment

日期：2014-11-17 阅读：911

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图像所系列学术报告

报告题目：Toward Understanding Human Actions in Unconstrained Environment

时间：11月18日上午10点半

地点：图像通信所大会议室（SEIEE-5-406）

摘要：Human action understanding is receiving extensive research interests in computer vision nowadays due to its wide applications in surveillance, human-computer interface, sports video analysis, and content based video retrieval. The challenges of action understanding come from background clutter, viewpoint changes, and motion and appearance variations. In this talk, we will show our recent works (CVPR13, ICCV13,CVPR 14, ECCV 14) to address these challenges. These works range from mining middle level parts, multi-view encoding, and utilizing deep architectures. Experimental results on large public datasets demonstrate the effectiveness of our approaches.

简历: Yu Qiao received Ph.D from the University of Electro-Communications, Japan, in 2006. He was a JSPS fellow and then a project assistant professor with the University of Tokyo from 2007 to 2010. Now he is a professor with the Shenzhen Institutes of Advanced Technology, the Chinese Academy of Science. His research interests include pattern recognition, computer vision, multimedia, image processing and machine learning. He has published more than 100 papers in these fields. He received the Lu Jiaxi young researcher award from the Chinese Academy of Science.

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