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

78 authors, including; Yuzhi Zhao

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

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

78 authors, including
, Yuzhi Zhao

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

85 Citations (Scopus)

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 × 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.

Original language	English
Title of host publication	Computer Vision – ECCV 2020 Workshops
Subtitle of host publication	Proceedings
Editors	Adrien Bartoli, Andrea Fusiello
Place of Publication	Cham
Publisher	Springer
Pages	5-40
Volume	Part III
ISBN (Electronic)	9783030670702
ISBN (Print)	9783030670696
DOIs	https://doi.org/10.1007/978-3-030-67070-2_1
Publication status	Published - Aug 2020
Event	16th European Conference on Computer Vision (ECCV 2020) - Virtual, Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020 https://eccv2020.eu/

Publication series

Name	Lecture Notes in Computer Science
Volume	12537
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th European Conference on Computer Vision (ECCV 2020)
Abbreviated title	ECCV 2020
Place	United Kingdom
City	Glasgow
Period	23/08/20 → 28/08/20
Internet address	https://eccv2020.eu/

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

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

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@inproceedings{ff71d3931b4d4c5398726eedd5d3f96a,

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 × 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 = "\{78 authors, including\} and 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 JungHeum Kang and Sung-Ho Bae and Yongwoo Kim and Yanyun Qu and Geun-Woo Jeon and Jun-Ho Choi and Jun-Hyuk Kim and Jong-Seok Lee and Steven Marty and Eric Marty and Dongliang Xiong and Siang Chen and Lin Zha and Jiande Jiang and Yuzhi Zhao",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).; 16th European Conference on Computer Vision (ECCV 2020), ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",

year = "2020",

month = aug,

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

language = "English",

isbn = "9783030670696",

volume = "Part III",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "5--40",

editor = "Adrien Bartoli and Andrea Fusiello",

booktitle = "Computer Vision – ECCV 2020 Workshops",

url = "https://eccv2020.eu/",

}

78 authors, including & Zhao, Y 2020, AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. in A Bartoli & A Fusiello (eds), Computer Vision – ECCV 2020 Workshops: Proceedings. vol. Part III, Lecture Notes in Computer Science, vol. 12537, Springer , Cham, pp. 5-40, 16th European Conference on Computer Vision (ECCV 2020), Glasgow, United Kingdom, 23/08/20. https://doi.org/10.1007/978-3-030-67070-2_1

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. / 78 authors, including ; Zhao, Yuzhi.
Computer Vision – ECCV 2020 Workshops: Proceedings. ed. / Adrien Bartoli; Andrea Fusiello. Vol. Part III Cham: Springer , 2020. p. 5-40 (Lecture Notes in Computer Science; Vol. 12537).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - AIM 2020 Challenge on Efficient Super-Resolution

T2 - 16th European Conference on Computer Vision (ECCV 2020)

AU - 78 authors, including

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, JungHeum

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 - Zhao, Yuzhi

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2020/8

Y1 - 2020/8

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

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85101569286&origin=recordpage

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

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

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783030670696

VL - Part III

T3 - Lecture Notes in Computer Science

SP - 5

EP - 40

BT - Computer Vision – ECCV 2020 Workshops

A2 - Bartoli, Adrien

A2 - Fusiello, Andrea

PB - Springer

CY - Cham

Y2 - 23 August 2020 through 28 August 2020

ER -

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

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