Sukrut Rao (PhD Student)

Sukrut Sridhar Rao

Address: Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Standort: E1 4 - 628
Telefon: +49 681 9325 2146
Fax: +49 681 9325 2099
E-mail: Get email via email

Personal Information

Web Pages: Personal, MPI
Publication Profiles: Google Scholar, DBLP, ORCiD
Social: LinkedIn, Twitter
Code: GitHub

Publications

2023

Conference paper

D2

S. Rao, M. Böhle, A. Parchami-Araghi, and B. Schiele

“Studying How to Efficiently and Effectively Guide Models with Explanations,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023), Paris, France.

mehr

BibTeX

@inproceedings{Rao_ICCV23,
TITLE = {Studying How to Efficiently and Effectively Guide Models with Explanations},
AUTHOR = {Rao, Sukrut and B{\"o}hle, Moritz and Parchami-Araghi, Amin and Schiele, Bernt},
LANGUAGE = {eng},
PUBLISHER = {IEEE},
YEAR = {2023},
PUBLREMARK = {Accepted},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023)},
ADDRESS = {Paris, France},
}

Endnote

%0 Conference Proceedings
%A Rao, Sukrut
%A B&#246;hle, Moritz
%A Parchami-Araghi, Amin
%A Schiele, Bernt
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Studying How to Efficiently and Effectively Guide Models with Explanations : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000D-CA7B-6
%D 2023
%B IEEE/CVF International Conference on Computer Vision
%Z date of event: 2023-10-02 - 2023-10-06
%C Paris, France
%B Proceedings of the IEEE/CVF International Conference on Computer Vision
%I IEEE

2022

Conference paper

D4D2

S. Rao, M. Böhle, and B. Schiele

“Towards Better Understanding Attribution Methods,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), New Orleans, LA, USA, 2022.

mehr

BibTeX

@inproceedings{Rao_CVPR2022,
TITLE = {Towards Better Understanding Attribution Methods},
AUTHOR = {Rao, Sukrut and B{\"o}hle, Moritz and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-6654-6946-3},
DOI = {10.1109/CVPR52688.2022.00998},
PUBLISHER = {IEEE},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)},
PAGES = {10213--10222},
ADDRESS = {New Orleans, LA, USA},
}

Endnote

%0 Conference Proceedings
%A Rao, Sukrut
%A B&#246;hle, Moritz
%A Schiele, Bernt
%+ Computer Graphics, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Towards Better Understanding Attribution Methods : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000A-6F91-6
%R 10.1109/CVPR52688.2022.00998
%D 2022
%B 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition
%Z date of event: 2022-06-19 - 2022-06-24
%C New Orleans, LA, USA
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 10213 - 10222
%I IEEE
%@ 978-1-6654-6946-3

2020

Conference paper

D4D2

S. Rao, D. Stutz, and B. Schiele

“Adversarial Training Against Location-Optimized Adversarial Patches,” in Computer Vision -- ECCV Workshops 2020, Glasgow, UK, 2021.

mehr

BibTeX

@inproceedings{DBLP:conf/eccv/RaoSS20,
TITLE = {Adversarial Training Against Location-Optimized Adversarial Patches},
AUTHOR = {Rao, Sukrut and Stutz, David and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-3-030-68237-8},
DOI = {10.1007/978-3-030-68238-5_32},
PUBLISHER = {Springer},
YEAR = {2020},
MARGINALMARK = {$\bullet$},
DATE = {2021},
BOOKTITLE = {Computer Vision -- ECCV Workshops 2020},
EDITOR = {Bartoli, Adrian and Fusiello, Andrea},
PAGES = {429--448},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {12539},
ADDRESS = {Glasgow, UK},
}

Endnote

%0 Conference Proceedings
%A Rao, Sukrut
%A Stutz, David
%A Schiele, Bernt
%+ Computer Graphics, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Adversarial Training Against Location-Optimized Adversarial Patches : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0008-1662-1
%R 10.1007/978-3-030-68238-5_32
%D 2021
%B 16th European Conference on Computer Vision
%Z date of event: 2020-08-23 - 2020-08-28
%C Glasgow, UK
%B Computer Vision -- ECCV Workshops 2020 
%E Bartoli, Adrian; Fusiello, Andrea
%P 429 - 448
%I Springer
%@ 978-3-030-68237-8
%B Lecture Notes in Computer Science
%N 12539