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2019年1月9日学术报告( Dr.Chang Xu,澳大利亚悉尼大学)
2019年01月07日08时 人评论

报告题目Generative Adversarial Networks for Cross-domain Visual Data

报告时间201919(周三)下午16:00

报告地点:计算机学院B403

报告人 Chang Xu

报告人单位:澳大利亚悉尼大学

 

报告人简介

Dr Chang Xu is a Lecturer in Machine Learning and Computer Vision at the School of Computer Science, University of Sydney. He obtained a Bachelor of Engineering from Tianjin University, China, and a Ph.D. degree from Peking University, China. While pursuing his PhD degree, Chang received fellowships from IBM and Baidu. His research interests lie in machine learning, data mining algorithms and related applications in artificial intelligence and computer vision, including multi-view learning, multi-label learning, visual search and face recognition. His research outcomes have been widely published in prestigious journals and top conference.

 

报告摘要

This talk will introduce generative adversarial networks (GAN) for cross-domain visual data. In particular, we focus on two kinds of tasks, image-to-image translation and image-to-video translation. In the first task, to relieve the burden of classical GAN, we develop a new attention GAN technique, which decomposes the generative network of classical GAN into two separated networks: the attention network predicting spatial attention maps of images, and the transformation network focusing on translating objects. To address the second task, we propose a two-stage unsupervised motion adaptation framework. We first extract motion information from source video clips, and then the learned motions are combined with different target image to synthesize desired videos. We devised a motion adaptation generative adversarial network and an image appearance adversarial network to evaluate the correctness of the synthesized frames motion and appearance, respectively. Experimental results on real-world datasets demonstrate the effectiveness of the proposed algorithms.

 

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