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Towards High Fidelity Face Frontalization in the Wild 期刊论文
International Journal of Computer Vision, 2019, 卷号: 128, 期号: 1, 页码: 1485–1504
作者:  Cao, Jie;  Hu, Yibo;  Zhang, Hongwen;  He, Ran;  Sun, Zhenan
Adobe PDF(3816Kb)  |  收藏  |  浏览/下载：187/54  |  提交时间：2021/05/26
Face frontalization  Realistic face generation  Pose-invariant face recognition  
Deep Learning Based Single Image Super-resolution: A Survey 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 413-426
作者:  Viet Khanh Ha;  Jin-Chang Ren;  Xin-Ying Xu;  Sophia Zhao;  Gang Xie;  Valentin Masero;  Amir Hussain
浏览  |  Adobe PDF(1389Kb)  |  收藏  |  浏览/下载：166/45  |  提交时间：2021/02/22
Image super-resolution  convolutional neural network  high-resolution image  low-resolution image  deep learning.  
Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 16-26
作者:  Tian-Xiang Zhang;  Jin-Ya Su;  Cun-Jia Liu;  Wen-Hua Chen
浏览  |  Adobe PDF(1123Kb)  |  收藏  |  浏览/下载：210/64  |  提交时间：2021/02/22
Sentinel-2A  remote sensing  image classification  supervised learning  precision agriculture.  
Learning a bi-level adversarial network with global and local perception for makeup-invariant face verification 期刊论文
Pattern Recognition, 2019, 卷号: 90, 期号: -, 页码: 99-108
作者:  Li, Yi;  Song, Lingxiao;  Wu, Xiang;  He, Ran;  Tan, Tieniu
浏览  |  Adobe PDF(2555Kb)  |  收藏  |  浏览/下载：256/76  |  提交时间：2020/06/10
Face verification  Makeup-invariant  Generative adversarial network  
Disentangled Representation Learning of Makeup Portraits in the Wild 期刊论文
International Journal of Computer Vision, 2019, 卷号: -, 期号: -, 页码: -
作者:  Li, Yi;  Huang, Huaibo;  Cao, Jie;  He, Ran;  Tan, Tieniu
浏览  |  Adobe PDF(24456Kb)  |  收藏  |  浏览/下载：312/70  |  提交时间：2020/06/10
Face verification  Makeup transfer  Disentangled feature  Correspondence field  
Adversarial image generation by combining content and style 期刊论文
IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 14, 页码: 2716-2723
作者:  Liu, Songyan;  Zhao, Chaoyang;  Gao, Yunze;  Wang, Jinqiao;  Tang, Ming
Adobe PDF(4319Kb)  |  收藏  |  浏览/下载：456/122  |  提交时间：2020/03/30
image recognition  feature extraction  learning (artificial intelligence)  image texture  adversarial image generation  unique style  reference images  style feature extraction module  style specific image generation model  double-cycle training strategy  natural-content pairs  input natural images  style exchange  style-exchanged images  licence-plate image  handbags images  
A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network 期刊论文
Physics in Medicine & Biology, 2019, 卷号: 64, 期号: 2019, 页码: 245010
作者:  Zhang,Zeyu;  Cai,Meishan;  Gao,Yuan;  Shi,Xiaojing;  Zhang,Xiaojun;  Hu,Zhenhua;  Tian,Jie
Adobe PDF(3210Kb)  |  收藏  |  浏览/下载：443/88  |  提交时间：2020/03/30
Cerenkov luminescence tomography (CLT)  optical reconstruction  photon propagation  neural network  inverse problem  
Towards Real-Time Advancement of Underwater Visual Quality With GAN 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 卷号: 66, 期号: 12, 页码: 9350-9359
作者:  Chen, Xingyu;  Yu, Junzhi;  Kong, Shihan;  Wu, Zhengxing;  Fang, Xi;  Wen, Li
浏览  |  Adobe PDF(4984Kb)  |  收藏  |  浏览/下载：495/161  |  提交时间：2019/12/16
Generative adversarial networks (GAN) image restoration  machine learning  underwater vision  
Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 10, 页码: 5105-5120
作者:  Li, Debang;  Wu, Huikai;  Zhang, Junge;  Huang, Kaiqi
Adobe PDF(6588Kb)  |  收藏  |  浏览/下载：410/48  |  提交时间：2019/12/16
Reinforcement learning  adversarial learning  image cropping  
Deep convolution network for surveillance records super-resolution 期刊论文
MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 卷号: 78, 期号: 17, 页码: 23815-23829
作者:  Shamsolmoali, Pourya;  Zareapoor, Masoumeh;  Jain, Deepak Kumar;  Jain, Vinay Kumar;  Yang, Jie
收藏  |  浏览/下载：222/0  |  提交时间：2019/12/16
Super-resolution  Convolution neural networks  Surveillance records  Deep learning