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; Jung Heum 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 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

doi:10.1007/978-3-030-67070-2_1

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, Jung Heum 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 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

计算机学院

Swiss Federal Institute of Technology Zurich
Nanjing University
Chinese Academy of Sciences
University of Electronic Science and Technology of China
McMaster University
Shenzhen Institute of Advanced Technology
Xiamen University
Indian Institute of Technology Madras
Nanjing University of Aeronautics and Astronautics
Northwestern Polytechnical University Xian
Kyung Hee University
Sangmyung University
Yonsei University
Zhejiang University
Ltd.)
Xidian University
Samsung AI Centre
Dalian Maritime University
China Everbright Bank Co., Ltd.
College of Engineering Trivandrum
InnoPeak Technology
SenseTime Group Limited
Sun Yat-Sen University
City University of Hong Kong
Guilin University of Electronic Technology
University of Udine

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

85 引用（Scopus）

摘要

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

源语言	英语
主期刊名	Computer Vision – ECCV 2020 Workshops, Proceedings
编辑	Adrien Bartoli, Andrea Fusiello
出版商	Springer Science and Business Media Deutschland GmbH
页	5-40
页数	36
ISBN（印刷版）	9783030670696
DOI	https://doi.org/10.1007/978-3-030-67070-2_1
出版状态	已出版 - 2020
活动	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, 英国期限: 23 8月 2020 → 28 8月 2020

丛书

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	12537 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
国家/地区	英国
市	Glasgow
时期	23/08/20 → 28/08/20

访问文件

10.1007/978-3-030-67070-2_1

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, K., Danelljan, M., Li, Y., Timofte, R., Liu, J., Tang, J., Wu, G., Zhu, Y., He, X., Xu, W., Li, C., Leng, C., Cheng, J., Wu, G., Wang, W., Liu, X., Zhao, H., Kong, X., He, J., ... Micheloni, C. (2020). AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. 在 A. Bartoli, & A. Fusiello (版本), Computer Vision – ECCV 2020 Workshops, Proceedings (页码 5-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 12537 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-67070-2_1

Zhang, Kai ; Danelljan, Martin ; Li, Yawei 等. / AIM 2020 Challenge on Efficient Super-Resolution : Methods and Results. Computer Vision – ECCV 2020 Workshops, Proceedings. 编辑 / Adrien Bartoli ; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2020. 页码 5-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b2ef0801c16f433094a8c01298b7983f,

title = "AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results",

abstract = "This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor {\texttimes} 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.",

author = "Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and Jie Liu and Jie Tang and Gangshan Wu and Yu Zhu and Xiangyu He and Wenjie Xu and Chenghua Li and Cong Leng and Jian Cheng and Guangyang Wu and Wenyi Wang and Xiaohong Liu and Hengyuan Zhao and Xiangtao Kong and Jingwen He and Yu Qiao and Chao Dong and Xiaotong Luo and Liang Chen and Jiangtao Zhang and Maitreya Suin and Kuldeep Purohit and Rajagopalan, \{A. N.\} and Xiaochuan Li and Zhiqiang Lang and Jiangtao Nie and Wei Wei and Lei Zhang and Abdul Muqeet and Jiwon Hwang and Subin Yang and Kang, \{Jung Heum\} and Bae, \{Sung Ho\} and Yongwoo Kim and Yanyun Qu and Jeon, \{Geun Woo\} and Choi, \{Jun Ho\} and Kim, \{Jun Hyuk\} and Lee, \{Jong Seok\} and Steven Marty and Eric Marty and Dongliang Xiong and Siang Chen and Lin Zha and Jiande Jiang and Xinbo Gao and Wen Lu and Haicheng Wang and Vineeth Bhaskara and Alex Levinshtein and Stavros Tsogkas and Allan Jepson and Xiangzhen Kong and Tongtong Zhao and Shanshan Zhao and Hrishikesh, \{P. S.\} and Densen Puthussery and Jiji, \{C. V.\} and Nan Nan and Shuai Liu and Jie Cai and Zibo Meng and Jiaming Ding and Ho, \{Chiu Man\} and Xuehui Wang and Qiong Yan and Yuzhi Zhao and Long Chen and Long Sun and Wenhao Wang and Zhenbing Liu and Rushi Lan and Umer, \{Rao Muhammad\} and Christian Micheloni",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",

year = "2020",

doi = "10.1007/978-3-030-67070-2\_1",

language = "英语",

isbn = "9783030670696",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "5--40",

editor = "Adrien Bartoli and Andrea Fusiello",

booktitle = "Computer Vision {\textendash} ECCV 2020 Workshops, Proceedings",

}

Zhang, K, Danelljan, M, Li, Y, Timofte, R, Liu, J, Tang, J, Wu, G, Zhu, Y, He, X, Xu, W, Li, C, Leng, C, Cheng, J, Wu, G, Wang, W, Liu, X, Zhao, H, Kong, X, He, J, Qiao, Y, Dong, C, Luo, X, Chen, L, Zhang, J, Suin, M, Purohit, K, Rajagopalan, AN, Li, X, Lang, Z, Nie, J, Wei, W , Zhang, L, Muqeet, A, Hwang, J, Yang, S, Kang, JH, Bae, SH, Kim, Y, Qu, Y, Jeon, GW, Choi, JH, Kim, JH, Lee, JS, Marty, S, Marty, E, Xiong, D, Chen, S, Zha, L, Jiang, J, Gao, X, Lu, W, Wang, H, Bhaskara, V, Levinshtein, A, Tsogkas, S, Jepson, A, Kong, X, Zhao, T, Zhao, S, Hrishikesh, PS, Puthussery, D, Jiji, CV, Nan, N, Liu, S, Cai, J, Meng, Z, Ding, J, Ho, CM, Wang, X, Yan, Q, Zhao, Y, Chen, L, Sun, L, Wang, W, Liu, Z, Lan, R, Umer, RM & Micheloni, C 2020, AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. 在 A Bartoli & A Fusiello (版本), Computer Vision – ECCV 2020 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 12537 LNCS, Springer Science and Business Media Deutschland GmbH, 页码 5-40, Workshops held at the 16th European Conference on Computer Vision, ECCV 2020, Glasgow, 英国, 23/08/20. https://doi.org/10.1007/978-3-030-67070-2_1

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. / Zhang, Kai; Danelljan, Martin; Li, Yawei 等.
Computer Vision – ECCV 2020 Workshops, Proceedings. 编辑 / Adrien Bartoli; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2020. 页码 5-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 12537 LNCS).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - AIM 2020 Challenge on Efficient Super-Resolution

T2 - Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

AU - Zhang, Kai

AU - Danelljan, Martin

AU - Li, Yawei

AU - Timofte, Radu

AU - Liu, Jie

AU - Tang, Jie

AU - Wu, Gangshan

AU - Zhu, Yu

AU - He, Xiangyu

AU - Xu, Wenjie

AU - Li, Chenghua

AU - Leng, Cong

AU - Cheng, Jian

AU - Wu, Guangyang

AU - Wang, Wenyi

AU - Liu, Xiaohong

AU - Zhao, Hengyuan

AU - Kong, Xiangtao

AU - He, Jingwen

AU - Qiao, Yu

AU - Dong, Chao

AU - Luo, Xiaotong

AU - Chen, Liang

AU - Zhang, Jiangtao

AU - Suin, Maitreya

AU - Purohit, Kuldeep

AU - Rajagopalan, A. N.

AU - Li, Xiaochuan

AU - Lang, Zhiqiang

AU - Nie, Jiangtao

AU - Wei, Wei

AU - Zhang, Lei

AU - Muqeet, Abdul

AU - Hwang, Jiwon

AU - Yang, Subin

AU - Kang, Jung Heum

AU - Bae, Sung Ho

AU - Kim, Yongwoo

AU - Qu, Yanyun

AU - Jeon, Geun Woo

AU - Choi, Jun Ho

AU - Kim, Jun Hyuk

AU - Lee, Jong Seok

AU - Marty, Steven

AU - Marty, Eric

AU - Xiong, Dongliang

AU - Chen, Siang

AU - Zha, Lin

AU - Jiang, Jiande

AU - Gao, Xinbo

AU - Lu, Wen

AU - Wang, Haicheng

AU - Bhaskara, Vineeth

AU - Levinshtein, Alex

AU - Tsogkas, Stavros

AU - Jepson, Allan

AU - Kong, Xiangzhen

AU - Zhao, Tongtong

AU - Zhao, Shanshan

AU - Hrishikesh, P. S.

AU - Puthussery, Densen

AU - Jiji, C. V.

AU - Nan, Nan

AU - Liu, Shuai

AU - Cai, Jie

AU - Meng, Zibo

AU - Ding, Jiaming

AU - Ho, Chiu Man

AU - Wang, Xuehui

AU - Yan, Qiong

AU - Zhao, Yuzhi

AU - Chen, Long

AU - Sun, Long

AU - Wang, Wenhao

AU - Liu, Zhenbing

AU - Lan, Rushi

AU - Umer, Rao Muhammad

AU - Micheloni, Christian

PY - 2020

Y1 - 2020

N2 - This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

AB - This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

UR - https://www.scopus.com/pages/publications/85101569286

U2 - 10.1007/978-3-030-67070-2_1

DO - 10.1007/978-3-030-67070-2_1

M3 - 会议稿件

AN - SCOPUS:85101569286

SN - 9783030670696

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 5

EP - 40

BT - Computer Vision – ECCV 2020 Workshops, Proceedings

A2 - Bartoli, Adrien

A2 - Fusiello, Andrea

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 23 August 2020 through 28 August 2020

ER -

Zhang K, Danelljan M, Li Y, Timofte R, Liu J, Tang J 等. AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. 在 Bartoli A, Fusiello A, 编辑, Computer Vision – ECCV 2020 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. 页码 5-40. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-67070-2_1

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

摘要

丛书

会议

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学术指纹

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