AIM 2020 Challenge on Image Extreme Inpainting

Evangelos Ntavelis; Andrés Romero; Siavash Bigdeli; Radu Timofte; Zheng Hui; Xiumei Wang; Xinbo Gao; Chajin Shin; Taeoh Kim; Hanbin Son; Sangyoun Lee; Chao Li; Fu Li; Dongliang He; Shilei Wen; Errui Ding; Mengmeng Bai; Shuchen Li; Yu Zeng; Zhe Lin; Jimei Yang; Jianming Zhang; Eli Shechtman; Huchuan Lu; Weijian Zeng; Haopeng Ni; Yiyang Cai; Chenghua Li; Dejia Xu; Haoning Wu; Yu Han; Uddin S.M. Nadim; Hae Woong Jang; Soikat Hasan Ahmed; Jungmin Yoon; Yong Ju Jung; Chu Tak Li; Zhi Song Liu; Li Wen Wang; Wan Chi Siu; Daniel P.K. Lun; Maitreya Suin; Kuldeep Purohit; A. N. Rajagopalan; Pratik Narang; Murari Mandal; Pranjal Singh Chauhan

doi:10.1007/978-3-030-67070-2_43

AIM 2020 Challenge on Image Extreme Inpainting

Evangelos Ntavelis, Andrés Romero, Siavash Bigdeli, Radu Timofte, Zheng Hui, Xiumei Wang, Xinbo Gao, Chajin Shin, Taeoh Kim, Hanbin Son, Sangyoun Lee, Chao Li, Fu Li, Dongliang He, Shilei Wen, Errui Ding, Mengmeng Bai, Shuchen Li, Yu Zeng, Zhe LinJimei Yang, Jianming Zhang, Eli Shechtman, Huchuan Lu, Weijian Zeng, Haopeng Ni, Yiyang Cai, Chenghua Li, Dejia Xu, Haoning Wu, Yu Han, Uddin S.M. Nadim, Hae Woong Jang, Soikat Hasan Ahmed, Jungmin Yoon, Yong Ju Jung, Chu Tak Li, Zhi Song Liu, Li Wen Wang, Wan Chi Siu, Daniel P.K. Lun, Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan, Pratik Narang, Murari Mandal, Pranjal Singh Chauhan

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

15 Citations (Scopus)

Abstract

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods.

Original language	English
Title of host publication	Computer Vision – ECCV 2020 Workshops, Proceedings
Editors	Adrien Bartoli, Andrea Fusiello
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	716-741
Number of pages	26
ISBN (Print)	9783030670696
DOIs	https://doi.org/10.1007/978-3-030-67070-2_43
Publication status	Published - Jan 2021
Event	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12537 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/Territory	United Kingdom
City	Glasgow
Period	23/08/20 → 28/08/20

Keywords

Extreme image inpainting
Generative modeling
Image synthesis

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/978-3-030-67070-2_43

Cite this

Ntavelis, E., Romero, A., Bigdeli, S., Timofte, R., Hui, Z., Wang, X., Gao, X., Shin, C., Kim, T., Son, H., Lee, S., Li, C., Li, F., He, D., Wen, S., Ding, E., Bai, M., Li, S., Zeng, Y., ... Chauhan, P. S. (2021). AIM 2020 Challenge on Image Extreme Inpainting. In A. Bartoli, & A. Fusiello (Eds.), Computer Vision – ECCV 2020 Workshops, Proceedings (pp. 716-741). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12537 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-67070-2_43

Ntavelis, Evangelos ; Romero, Andrés ; Bigdeli, Siavash et al. / AIM 2020 Challenge on Image Extreme Inpainting. Computer Vision – ECCV 2020 Workshops, Proceedings. editor / Adrien Bartoli ; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2021. pp. 716-741 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{302badf076dc475881f4ae574af8b79d,

title = "AIM 2020 Challenge on Image Extreme Inpainting",

abstract = "This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods.",

keywords = "Extreme image inpainting, Generative modeling, Image synthesis",

author = "Evangelos Ntavelis and Andr{\'e}s Romero and Siavash Bigdeli and Radu Timofte and Zheng Hui and Xiumei Wang and Xinbo Gao and Chajin Shin and Taeoh Kim and Hanbin Son and Sangyoun Lee and Chao Li and Fu Li and Dongliang He and Shilei Wen and Errui Ding and Mengmeng Bai and Shuchen Li and Yu Zeng and Zhe Lin and Jimei Yang and Jianming Zhang and Eli Shechtman and Huchuan Lu and Weijian Zeng and Haopeng Ni and Yiyang Cai and Chenghua Li and Dejia Xu and Haoning Wu and Yu Han and Nadim, {Uddin S.M.} and Jang, {Hae Woong} and Ahmed, {Soikat Hasan} and Jungmin Yoon and Jung, {Yong Ju} and Li, {Chu Tak} and Liu, {Zhi Song} and Wang, {Li Wen} and Siu, {Wan Chi} and Lun, {Daniel P.K.} and Maitreya Suin and Kuldeep Purohit and Rajagopalan, {A. N.} and Pratik Narang and Murari Mandal and Chauhan, {Pranjal Singh}",

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 = "2021",

month = jan,

doi = "10.1007/978-3-030-67070-2_43",

language = "English",

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 = "716--741",

editor = "Adrien Bartoli and Andrea Fusiello",

booktitle = "Computer Vision – ECCV 2020 Workshops, Proceedings",

address = "Germany",

}

Ntavelis, E, Romero, A, Bigdeli, S, Timofte, R, Hui, Z, Wang, X, Gao, X, Shin, C, Kim, T, Son, H, Lee, S, Li, C, Li, F, He, D, Wen, S, Ding, E, Bai, M, Li, S, Zeng, Y, Lin, Z, Yang, J, Zhang, J, Shechtman, E, Lu, H, Zeng, W, Ni, H, Cai, Y, Li, C, Xu, D, Wu, H, Han, Y, Nadim, USM, Jang, HW, Ahmed, SH, Yoon, J, Jung, YJ, Li, CT, Liu, ZS, Wang, LW, Siu, WC, Lun, DPK, Suin, M, Purohit, K, Rajagopalan, AN, Narang, P, Mandal, M & Chauhan, PS 2021, AIM 2020 Challenge on Image Extreme Inpainting. in A Bartoli & A Fusiello (eds), Computer Vision – ECCV 2020 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12537 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 716-741, Workshops held at the 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 23/08/20. https://doi.org/10.1007/978-3-030-67070-2_43

AIM 2020 Challenge on Image Extreme Inpainting. / Ntavelis, Evangelos; Romero, Andrés; Bigdeli, Siavash et al.
Computer Vision – ECCV 2020 Workshops, Proceedings. ed. / Adrien Bartoli; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2021. p. 716-741 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12537 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - AIM 2020 Challenge on Image Extreme Inpainting

AU - Ntavelis, Evangelos

AU - Romero, Andrés

AU - Bigdeli, Siavash

AU - Timofte, Radu

AU - Hui, Zheng

AU - Wang, Xiumei

AU - Gao, Xinbo

AU - Shin, Chajin

AU - Kim, Taeoh

AU - Son, Hanbin

AU - Lee, Sangyoun

AU - Li, Chao

AU - Li, Fu

AU - He, Dongliang

AU - Wen, Shilei

AU - Ding, Errui

AU - Bai, Mengmeng

AU - Li, Shuchen

AU - Zeng, Yu

AU - Lin, Zhe

AU - Yang, Jimei

AU - Zhang, Jianming

AU - Shechtman, Eli

AU - Lu, Huchuan

AU - Zeng, Weijian

AU - Ni, Haopeng

AU - Cai, Yiyang

AU - Li, Chenghua

AU - Xu, Dejia

AU - Wu, Haoning

AU - Han, Yu

AU - Nadim, Uddin S.M.

AU - Jang, Hae Woong

AU - Ahmed, Soikat Hasan

AU - Yoon, Jungmin

AU - Jung, Yong Ju

AU - Li, Chu Tak

AU - Liu, Zhi Song

AU - Wang, Li Wen

AU - Siu, Wan Chi

AU - Lun, Daniel P.K.

AU - Suin, Maitreya

AU - Purohit, Kuldeep

AU - Rajagopalan, A. N.

AU - Narang, Pratik

AU - Mandal, Murari

AU - Chauhan, Pranjal Singh

PY - 2021/1

Y1 - 2021/1

N2 - This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods.

AB - This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods.

KW - Extreme image inpainting

KW - Generative modeling

KW - Image synthesis

UR - http://www.scopus.com/inward/record.url?scp=85101535092&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-67070-2_43

DO - 10.1007/978-3-030-67070-2_43

M3 - Conference article published in proceeding or book

AN - SCOPUS:85101535092

SN - 9783030670696

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

SP - 716

EP - 741

BT - Computer Vision – ECCV 2020 Workshops, Proceedings

A2 - Bartoli, Adrien

A2 - Fusiello, Andrea

PB - Springer Science and Business Media Deutschland GmbH

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

Y2 - 23 August 2020 through 28 August 2020

ER -

Ntavelis E, Romero A, Bigdeli S, Timofte R, Hui Z, Wang X et al. AIM 2020 Challenge on Image Extreme Inpainting. In Bartoli A, Fusiello A, editors, Computer Vision – ECCV 2020 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 716-741. (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_43

AIM 2020 Challenge on Image Extreme Inpainting

Abstract

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Keywords

ASJC Scopus subject areas

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