Frank R. Schmidt

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Daniel CremersTechnical University of MunichVerified email at tum.de
Zico KolterCarnegie Mellon UniversityVerified email at cs.cmu.edu
Yuri BoykovProfessor, Computer Science, University of WaterlooVerified email at uwaterloo.ca
Lena GorelickResearch Scientist at University of WaterlooVerified email at uwo.ca
Jan Hendrik MetzenBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Thomas BroxUniversity of Freiburg and AmazonVerified email at cs.uni-freiburg.de
Thomas WindheuserTechnische Universität MünchenVerified email at in.tum.de
Ulrich SchlickeweiTechnische Hochschule IngolstadtVerified email at thi.de
Victor LempitskyCinemersive LabsVerified email at cinemersivelabs.com
Fredrik KahlProfessor, Chalmers University of TechnologyVerified email at chalmers.se
Naveen Shankar NagarajaSenior Machine Learning Engineer, BMW GroupVerified email at informatik.uni-freiburg.de
Andrew DelongAssistant Professor, Concordia UniversityVerified email at concordia.ca
Alan YuilleProfessor of Cognitive Science and Computer Science, Johns Hopkins UniversityVerified email at jhu.edu
Csaba DomokosBosch Center for Artificial intelligenceVerified email at de.bosch.com
Olga VekslerSchool of Computer Science, University of WaterlooVerified email at uwaterloo.ca
Maddalena StrumiaSiemens HealthcareVerified email at siemens-healthineers.com
Emanuele RodolàProfessor of Computer Science, Sapienza University of RomeVerified email at di.uniroma1.it
Zorah LähnerPostdoctoral Researcher, University of SiegenVerified email at uni-siegen.de
Michael BronsteinDeepMind Professor of AI, University of OxfordVerified email at cs.ox.ac.uk
Jan-Hendrik LangeMax Planck Institute for InformaticsVerified email at mpi-inf.mpg.de

Frank R. Schmidt

Bosch Center for Artificial Intelligence

Verified email at de.bosch.com - Homepage

Artificial Intelligence


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Scaling provable adversarial defenses E Wong, F Schmidt, JH Metzen, JZ Kolter Advances in Neural Information Processing Systems, 8400-8409, 2018	400	2018
Wasserstein adversarial examples via projected sinkhorn iterations E Wong, F Schmidt, Z Kolter International Conference on Machine Learning, 6808-6817, 2019	182	2019
Shape priors in variational image segmentation: Convexity, lipschitz continuity and globally optimal solutions D Cremers, FR Schmidt, F Barthel 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-6, 2008	162	2008
Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition D Cremers, Y Boykov, A Blake, FR Schmidt Springer-Verlag, 2009	149*	2009
Video Segmentation with Just a Few Strokes N Shankar Nagaraja, FR Schmidt, T Brox Proceedings of the IEEE International Conference on Computer Vision, 3235-3243, 2015	134	2015
Adversarial camera stickers: A physical camera-based attack on deep learning systems J Li, F Schmidt, Z Kolter International Conference on Machine Learning, 3896-3904, 2019	108	2019
Efficient planar graph cuts with applications in computer vision FR Schmidt, E Töppe, D Cremers Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on …, 2009	98	2009
Geometrically consistent elastic matching of 3d shapes: A linear programming solution T Windheuser, U Schlickewei, FR Schmidt, D Cremers 2011 International Conference on Computer Vision, 2134-2141, 2011	78	2011
Fast matching of planar shapes in sub-cubic runtime FR Schmidt, D Farin, D Cremers 2007 11th IEEE International Conference on Computer Vision, 1-6, 2007	71	2007
Fast trust region for segmentation L Gorelick, FR Schmidt, Y Boykov Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013	52	2013
Shape matching by variational computation of geodesics on a manifold FR Schmidt, M Clausen, D Cremers Joint Pattern Recognition Symposium, 142-151, 2006	47	2006
Large‐Scale Integer Linear Programming for Orientation Preserving 3D Shape Matching T Windheuser, U Schlickwei, FR Schmidt, D Cremers Computer Graphics Forum 30 (5), 1471-1480, 2011	33	2011
Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition Y Boykov, FR Schmidt, F Kahl, V Lempitsky Springer-Verlag, 2011	33*	2011
Segmentation with non-linear regional constraints via line-search cuts L Gorelick, F Schmidt, Y Boykov, A Delong, A Ward Computer Vision–ECCV 2012, 583-597, 2012	26	2012
Interactive segmentation with super-labels A Delong, L Gorelick, FR Schmidt, O Veksler, Y Boykov International Workshop on Energy Minimization Methods in Computer Vision and …, 2011	26	2011
White matter MS-lesion segmentation using a geometric brain model M Strumia, FR Schmidt, C Anastasopoulos, C Granziera, G Krueger, ... IEEE transactions on medical imaging 35 (7), 1636-1646, 2016	25	2016
Efficient globally optimal 2d-to-3d deformable shape matching Z Lahner, E Rodola, FR Schmidt, MM Bronstein, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016	25	2016
Efficient shape matching via graph cuts F Schmidt, E Töppe, D Cremers, Y Boykov Energy Minimization Methods in Computer Vision and Pattern Recognition, 39-54, 2007	21	2007
Hausdorff distance constraint for multi-surface segmentation FR Schmidt, Y Boykov European Conference on Computer Vision, 598-611, 2012	16	2012
A combinatorial solution to non-rigid 3d shape-to-image matching F Bernard, FR Schmidt, J Thunberg, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017	15	2017

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