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20171129-27:黄伟林 Learn CNNs from Large-scale Web Images without human annotat ...

2017-11-9 17:04| 发布者: 程一-计算所| 查看: 401| 评论: 0

摘要: 报告嘉宾:黄伟林(University of Oxford)报告时间:2017年11月29日(星期三)晚19:00(北京时间)报告题目:Learn CNNs from Large-scale Web Images without human annotation主持人:欧阳万里(悉尼大学)报告摘 ...

报告嘉宾:黄伟林(University of Oxford)

报告时间:2017年11月29日(星期三)晚19:00(北京时间)

报告题目:Learn CNNs from Large-scale Web Images without human annotation

主持人:欧阳万里(悉尼大学)


报告摘要:

Architectural scenes exhibit rich geometric patterns and regularities. I will present a sequence of our research that exploits geometric regularities for reconstructing high-quality 3D models from raw imaging sensor data. The classes of geometric priors range from low level geometric primitives such as lines, planes, or cuboids to high-level scene grammars constraining the reconstruction process.


报告人简介:

Weilin Huang is Chief Scientist of Malong Technologies. He was working as a postdoc researcher with Prof. Andrew Zisserman in Visual Geometry Group (VGG),  University of Oxford.  He was an Assistant Professor with the Chinese Academy of Science. He received his Ph.D. degree  from The University of Manchester, U.K. His research interests include scene text detection/recognition, large-scale image classification and medical image analysis. He has served as a PC Member or Reviewer for main computer vision conferences, including ICCV, CVPR, ECCV and AAAI. His team was the first runner-up at the ImageNet 2015 on scene recognition, and was the winner of WebVision Challenge in CVPR 2017.


Personal homepage of the speaker:

http://www.whuang.org/


特别鸣谢本次Webinar主要组织者:

VOOC责任委员:欧阳万里(悉尼大学)

VODB协调理事:卢孝强(中国科学院西安光学精密机械研究所)


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