报告时间：6月29日15:30

报告地点：计算机新大楼A411

报告题目：End-to-end learning face super-resolution with facial priors

主 讲 人 ：Chunhua Shen

内容简介：

Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To further generate realistic faces, we propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Moreover, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively.

主讲人简介：

Chunhua Shen is a Professor at School of Computer Science, University of Adelaide. He is a Project Leader and Chief Investigator at the Australian Research Council Centre of Excellence for Robotic Vision (ACRV), for which he leads the project on machine learning for robotic vision. Before he moved to Adelaide as a Senior Lecturer, he was with the computer vision program at NICTA (National ICT Australia), Canberra Research Laboratory for about six years. His research interests are in the intersection of computer vision and statistical machine learning. Recent work has been on large-scale image retrieval and classification, object detection and pixel labelling using deep learning.

He studied at Nanjing University, at Australian National University, and received his PhD degree from the University of Adelaide. From 2012 to 2016, he holds an Australian Research Council Future Fellowship. He served as Associate Editor of IEEE Transactions on Neural Networks and Learning Systems.

沈春华博士现任澳大利亚阿德雷德大学计算机科学学院教授(终身教职)。2011之前在澳大利亚国家信息通讯技术研究院堪培拉实验室的计算机视觉组工作近6年。目前主要从事统计机器学习以及计算机视觉领域的研究工作。主持多项科研课题，在重要国际学术期刊和会议发表论文100余篇。2015,2016年担任IEEE Transactions on Neural Networks and Learning Systems 副主编。多次担任重要国际学术会议(ICCV, CVPR, ECCV等)程序委员。

他曾在南京大学(本科及硕士)，澳大利亚国立大学(硕士)学习，并在阿德雷德大学获得计算机视觉方向的博士学位。2012年被澳大利亚研究理事会(Australian Research Council)授予Future Fellowship。

更多信息见: cs.adelaide.edu.au/~chhshen/