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Wenjie Xu
Researcher at Chinese Academy of Sciences
Publications - 3
Citations - 101
Wenjie Xu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Computational intelligence. The author has an hindex of 2, co-authored 2 publications receiving 52 citations.
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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang,Martin Danelljan,Yawei Li,Radu Timofte,Jie Liu,Jie Tang,Gangshan Wu,Yu Zhu,Xiangyu He,Wenjie Xu,Chenghua Li,Cong Leng,Jian Cheng,Guangyang Wu,Wenyi Wang,Xiaohong Liu,Hengyuan Zhao,Xiangtao Kong,Jingwen He,Yu Qiao,Chao Dong,Xiaotong Luo,Liang Chen,Jiangtao Zhang,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Xiaochuan Li,Zhiqiang Lang,Jiangtao Nie,Wei Wei,Lei Zhang,Abdul Muqeet,Jiwon Hwang,Subin Yang,JungHeum Kang,Sung-Ho Bae,Yongwoo Kim,Yanyun Qu,Geun-Woo Jeon,Jun-Ho Choi,Jun-Hyuk Kim,Jong-Seok Lee,Steven Marty,Eric Marty,Dongliang Xiong,Siang Chen,Lin Zha,Jiande Jiang,Xinbo Gao,Wen Lu,Haicheng Wang,Vineeth Bhaskara,Alex Levinshtein,Stavros Tsogkas,Allan D. Jepson,Xiangzhen Kong,Tongtong Zhao,Shanshan Zhao,Hrishikesh P S,Densen Puthussery,C. V. Jiji,Nan Nan,Shuai Liu,Jie Cai,Zibo Meng,Jiaming Ding,Chiu Man Ho,Xuehui Wang,Qiong Yan,Yuzhi Zhao,Long Chen,Long Sun,Wenhao Wang,Zhenbing Liu,Rushi Lan,Rao Muhammad Umer,Christian Micheloni +77 more
TL;DR: The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
Book ChapterDOI
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang,Martin Danelljan,Yawei Li,Radu Timofte,Jie Liu,Jie Tang,Gangshan Wu,Yu Zhu,Xiangyu He,Wenjie Xu,Chenghua Li,Cong Leng,Jian Cheng,Guangyang Wu,Wenyi Wang,Xiaohong Liu,Hengyuan Zhao,Xiangtao Kong,Jingwen He,Yu Qiao,Chao Dong,Xiaotong Luo,Liang Chen,Jiangtao Zhang,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Xiaochuan Li,Zhiqiang Lang,Jiangtao Nie,Wei Wei,Lei Zhang,Abdul Muqeet,Jiwon Hwang,Subin Yang,JungHeum Kang,Sung-Ho Bae,Yongwoo Kim,Yanyun Qu,Geun-Woo Jeon,Jun-Ho Choi,Jun-Hyuk Kim,Jong-Seok Lee,Steven Marty,Eric Marty,Dongliang Xiong,Siang Chen,Lin Zha,Jiande Jiang,Xinbo Gao,Wen Lu,Haicheng Wang,Vineeth Bhaskara,Alex Levinshtein,Stavros Tsogkas,Allan D. Jepson,Xiangzhen Kong,Tongtong Zhao,Shanshan Zhao,P. S. Hrishikesh,Densen Puthussery,C. V. Jiji,Nan Nan,Shuai Liu,Jie Cai,Zibo Meng,Jiaming Ding,Chiu Man Ho,Xuehui Wang,Qiong Yan,Yuzhi Zhao,Long Chen,Long Sun,Wenhao Wang,Zhenbing Liu,Rushi Lan,Rao Muhammad Umer,Christian Micheloni +77 more
TL;DR: The AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results as discussed by the authors was held in 2019, where the goal was to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption.
Journal ArticleDOI
Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning
TL;DR: In this article , a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm.