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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: 4495| 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)


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



We present a simple yet efficient approach capable of training deep neural networks on large-scale weaklysupervised web images, which are crawled from the Internet by using text queries, without any human annotation. We develop a principled learning strategy by leveraging curriculum learning, with the goal of handling massive amount of noisy labels and data unbalance effectively. We introduce a new curriculum design method that measures data complexity using cluster densities, and rank it in an unsupervised manner, allowing for an efficient implementation of curriculum learning in our large-scale web images. This results in a high-performance CNN model, where the negative impact of noisy labels is reduced substantially. Importantly, we show by experiments that those images with highly noisy labels surprisingly improve the generalization capability of model, by working as a manner of regularization. Our approaches, with an ensemble of multiple models, obtain an accuracy of 94.75% on the 1000-category image classification task, which won the 1st place in the Webvision Challenge, by outperforming the other submissions by a large margin.


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.

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