行健讲坛学术讲座
时间: 2018年12月17日（周一）上午10：00
所在: 校本部东区翔英楼T706室
讲座：基于麋集网络的图像超区分率
演讲者：A/ Prof. Jian Zhang, 澳大利亚悉尼科技大学（UTS）
演讲者简介：Dr. Jian Zhang received the BSc. degree from East China Normal University, Shanghai, China, in 1982; the MSc. degree in computer science from Flinders University, Adelaide, Australia, in 1994; and the Ph.D. degree in electrical engineering from the University of New South Wales (UNSW), Sydney, Australia, in 1999.
From 1997 to 2003, he was with the Visual Information Processing Laboratory, Motorola Labs, Sydney, as a Senior Research Engineer, and later became a Principal Research Engineer and a Foundation Manager with the Visual Communications Research Team. From 2004 to July 2011, he was a Principal Researcher and a Project Leader with National ICT Australia, Sydney. He is currently an Associate Professor with the Global Big Data Technologies Centre, School of Electrical & Data Engineering, Faculty of engineering and Information Technology, University of Technology Sydney, Sydney. Prof Zhang’s research interests include multimedia signal processing, computer vision, pattern recognition, visual information mining, human-computer interaction and intelligent video surveillance systems. Prof Zhang has co-authored more than 130 paper publications, book chapters, patents and technical reports from his research output, he was the co-author of eight granted US and China patents.
Dr. Zhang is an IEEE Senior Member. He was Technical Program Chair, 2008 IEEE Multimedia Signal Processing Workshop; Associated Editor, IEEE Transactions on Multimedia; Associated Editor, IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT); Associated Editor, EURASIP Journal on Image and Video Processing. As a General Co-Chair, Jian chaired the International Conference on Multimedia and Expo (ICME 2012) in Melbourne Australia 2012. As a Technical Program Co-Chair, Jian chaired The IEEE Visual Communications and Image Processing (IEEE VCIP 2014).
讲座摘要：图像超区分率是一类增添图像区分率的手艺，，，，，，被普遍应用于需要高清图像的图像处置惩罚手艺中。。。 。。最近，，，，，，深度学习要领被证实能有用处置惩罚图像超区分率问题。。。 。。其中，，，，，，深麋集网络在图像超区分率上取得了很好的效果，，，，，，这得益于麋集层之间特征重用。。。 。。可是，，，，，，通俗麋集网络限制了块之间的特征重用。。。 。。因此我们提出了用于图像超区分率的双麋集网络来提升特征学习性能。。。 。。这个双麋集网络通过加入了我们所提出块间麋集毗连，，，，，，来扩展原本的块内的麋集毗连。。。 。。这样，，，，，，特征信息不但只撒播到随后的一个块内，，，，，，而是传给厥后的所有块。。。 。。因此，，，，，，在深度网络训练历程中的梯度和特征消逝的问题可以被解决。。。 。。另外，，，，，，我们发明训练用于图像超区分率的通俗麋集网络，，，，，，要消耗大宗内存。。。 。。为了镌汰训练时的内存占用，，，，，，构建更深的网络，，，，，， 我们引入共享内存分派的要领，，，，，，来构建适用于图像超区分率的内存优化的麋集网络。。。 。。在果真的基准的数据集上的评测效果体现，，，，，，我们所提出的模子在使用适中的参数目和盘算量的情形下，，，，，，凌驾现在最新的超区分率要领的图像重修效果。。。 。。
约请人：8188cc威尼斯通讯与信息工程学院 安平 教授
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