报告题目：协作智能下的多媒体特征学习 （Learning Multimedia Features from Collective Intelligence）

报告人：张含望 博士

单位：新加坡国立大学-清华大学NExT研究中心

报告时间：2016年5月17日(周二) 9:30-11:00

报告地点：逸夫楼408会议室

报告人简介：张含望博士是新加坡国立大学-清华大学联合组建研究中心NExT的研究员，2014年在新加坡国立大学获得博士学位，致力于多媒体与机器视觉领域研究。张博士在CVPR、ICCV、ACM Multimedia、SIGIR、AAAI、TIP、TOMMCAP等多个顶级国际会议和期刊发表论文二十余篇（包括近十次口头报告），曾获得多媒体领域顶级国际会议ACM Multimedia最佳演示提名奖（2012），最佳学生论文奖（2013），以及新加坡国立大学计算机学院最佳博士论文奖（2014）。张博士担任了Multimedia Tools and Applications、Neurocomputing等多个国际期刊编委或客座主编，亦是TIP、TMM、TCSVT、TOMMCAP等顶尖学术期刊审稿人。

报告摘要：Traditional feature learning requires a considerable amount of well-annotated data (e.g., ImageNet), whose construction per se is expensive and time-consuming. Unfortunately, these data hardly keep up with the ever-evolving trends in multimedia applications, such as the target domain shift and novel semantic concepts. In this talk, I will share our recent research progress in learning features from collective intelligence, which is naively collected from the inexhaustible Web user-generated contents and behaviors like Facebook "like", Google "click" and Pinterest "pin". In fact, our research is a more aggressive and practical implementation of weakly-supervised and unsupervised learning. We will explore several interesting tasks on how to discover meaningful semantics from user behaviors and try to find the underlying rationales. Last but not the least, some interesting future directions will be prospected.

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