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【17-02期VALSEWebinar活动】

(2017-01-09 23:12:51)
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【17-02期VALSEWebinar活动】
报告嘉宾1:Jia Deng(University of Michigan)
报告时间:2017年01月11日(星期三)晚上8:00(北京时间)
报告题目:Going Deeper in Semantics and Mid-Level Vision
主持人:程明明(南开大学) 

报告摘要:
Achieving human-level visual understanding requires extracting deeper semantics from images. In particular, it entails moving beyond detecting objects to understanding the relations between them. It also demands progress in mid-level vision, which extracts deeper geometric information such as pose and 3D. In this talk I will present recent work on both fronts. I will describe efforts on recognizing human-object interactions, an important type of relations between visual entities. I will present a state-of-the-art method on human pose estimation. Finally, I will discuss recovering 3D from a single image, a fundamental mid-level vision problem.

参考文献:
http://web.eecs.umich.edu/~jiadeng/ 

报告人简介:
Jia Deng is an Assistant Professor of Computer Science and Engineering at the University of Michigan. His research focus is on computer vision and machine learning, in particular, achieving human-level visual understanding by integrating perception, cognition, and learning. He received his Ph.D. from Princeton University and his B.Eng. from Tsinghua University, both in computer science. He is a recipient of the Yahoo ACE Award, a Google Faculty Research Award, the ICCV Marr Prize, and the ECCV Best Paper Award.

Jia Deng, Ph.D.
Assistant Professor
Computer Science and Engineering
University of Michigan
http://web.eecs.umich.edu/~jiadeng/


【17-02期VALSEWebinar活动】
报告嘉宾2:Jun-Yan Zhu(UC Berkeley)
报告时间:2017年01月11日(星期三)晚上9:00(北京时间)
报告题目:Deep Learning for Visual Synthesis and Manipulation
主持人:程明明(南开大学)

报告摘要:
Realistic image synthesis and manipulation is challenging because it requires generating and modifying the image appearance in a user-controlled way, while preserving the realism of the result. Unless the user has considerable artistic skill, it is easy to "fall off" the manifold of natural images during generation and editing. In this talk, we propose to learn the natural image manifold directly from data using deep neural networks. We then define a class of image generation and editing operations, and constrain their output to lie on that learned manifold at all times. We present three different approaches: (1) Deep discriminative model: we train a discriminative CNN classifier to predict the realism of the generated result, and optimize an image generation pipeline to maximize the predicted realism score; (2) Deep generative model:  we propose to model the natural image manifold directly via a generative adversarial neural network, and constrain the output to be generated by the generative model; (3) Image-to-Image network: we train a network to map user inputs directly to the final results.

参考文献:
[1] Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman and Alexei A. Efros "Generative Visual Manipulation on the Natural Image Manifold" In ECCV 2016.
[2] Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman and Alexei A. Efros "Learning a Discriminative Model for the Perception of Realism in Composite Images" In ICCV 2015
[3] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros "Image-to-Image Translation with Conditional Adversarial Nets" In arxiv 2016
[4] Jun-Yan Zhu, Yong Jae Lee and Alexei A. Efros, "AverageExplorer: Interactive Exploration and Alignment of Visual Data Collections" In SIGGRAPH 2014

报告人简介:
Jun-Yan is a Computer Science Ph.D. student at UC Berkeley. He received his B.E from Tsinghua University in 2012. Jun-Yan is now working on computer graphics and computer vision with Professor Alexei A. Efros. His current research focuses on summarizing, mining and exploring large-scale visual data, with the goal of building a digital bridge between Humans and Big Visual Data. Jun-Yan is currently supported by a Facebook Fellowship. For more details, visit: www.eecs.berkeley.edu/~junyanz/  


特别鸣谢本次Webinar主要组织者:
VOOC责任委员:李策(兰州理工),张天柱(中科院自动化所)
VODB协调理事:白翔(华中科大),董乐(成电)


活动参与方式:
1、VALSE Webinar活动全部网上依托VALSE QQ群的“群视频”功能在线进行,活动时讲者会上传PPT或共享屏幕,听众可以看到Slides,听到讲者的语音,并通过文字或语音与讲者交互;
2、为参加活动,需加入VALSE QQ群,目前A、B、C、D群已满,除讲者等嘉宾外,只能申请加入VALSE E群,群号:279398311 。申请加入时需验证姓名、单位和身份,缺一不可。入群后,请实名,姓名身份单位。身份:学校及科研单位人员T;企业研发I;博士D;硕士M
3、为参加活动,请下载安装Windows QQ最新版,群视频不支持非Windows的系统,如Mac,Linux等,手机QQ可以听语音,但不能看视频slides;
4、在活动开始前10分钟左右,主持人会开启群视频,并发送邀请各群群友加入的链接,参加者直接点击进入即可;
5、活动过程中,请勿送花、棒棒糖等道具,也不要说无关话语,以免影响活动正常进行;
6、活动过程中,如出现听不到或看不到视频等问题,建议退出再重新进入,一般都能解决问题;
7、建议务必在速度较快的网络上参加活动,优先采用有线网络连接。

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