Thomas S.A. Wallis

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Matthias BethgeTübingen University & Maddox Co-FounderVerified email at bethgelab.org
Matthias KümmererTübingen AI Center, University TuebingenVerified email at bethgelab.org
Leon A. GatysApple Inc.Verified email at bethgelab.org
Christina Maria FunkeUniversity of TübingenVerified email at bethgelab.org
Felix WichmannEberhard Karls Universität TübingenVerified email at uni-tuebingen.de
Judith BorowskiPhD student, University of Tübingen & International Max Planck Research School for IntelligentVerified email at bethgelab.org
Wieland BrendelFellow at ELLIS Institut Tübingen, Group Leader, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Alexander S. EckerUniversity of Göttingen, GermanyVerified email at cs.uni-goettingen.de
Derek H. ArnoldSchool of Psychology, The University of QueenslandVerified email at psy.uq.edu.au
Mark HorswillThe University of QueenslandVerified email at psy.uq.edu.au
Roland Simon ZimmermannUniversity of TübingenVerified email at uni-tuebingen.de
Robert GeirhosResearch Scientist, Google DeepMindVerified email at google.com
Marek A. PedziwiatrJagiellonian UniversityVerified email at uj.edu.pl
Jason B. MattingleyThe University of QueenslandVerified email at uq.edu.au
William J. HarrisonSchool of Health, University of the Sunshine CoastVerified email at usc.edu.au
Philip GroveThe University of QueenslandVerified email at psy.uq.edu.au
Michael DorrAdaptive Sensory TechnologyVerified email at tum.de
Christopher Patrick TaylorNew England College of OptometryVerified email at neco.edu
Matthias TangemannPhD Candidate, University of TübingenVerified email at uni-tuebingen.de
Warrick RoseboomDepartment of Informatics and School of Psychology, University of SussexVerified email at sussex.ac.uk

Thomas S.A. Wallis

Centre for Cognitive Science, Technical University of Darmstadt

Verified email at tu-darmstadt.de

Vision Science Psychophysics Computational Modeling of Vision Machine Learning Statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Understanding low-and high-level contributions to fixation prediction M Kummerer, TSA Wallis, LA Gatys, M Bethge Proceedings of the IEEE international conference on computer vision, 4789-4798, 2017	630*	2017
Using fuzzy signal detection theory to determine why experienced and trained drivers respond faster than novices in a hazard perception test TSA Wallis, MS Horswill Accident Analysis & Prevention 39 (6), 1177-1185, 2007	247	2007
Information-theoretic model comparison unifies saliency metrics M Kümmerer, TSA Wallis, M Bethge Proceedings of the National Academy of Sciences 112 (52), 16054-16059, 2015	191	2015
Saliency benchmarking made easy: Separating models, maps and metrics M Kummerer, TSA Wallis, M Bethge Proceedings of the European Conference on Computer Vision (ECCV), 770-787, 2018	127	2018
Five points to check when comparing visual perception in humans and machines CM Funke, J Borowski, K Stosio, W Brendel, TSA Wallis, M Bethge Journal of Vision 21 (3), 16-16, 2021	96*	2021
A parametric texture model based on deep convolutional features closely matches texture appearance for humans TSA Wallis, CM Funke, AS Ecker, LA Gatys, FA Wichmann, M Bethge Journal of vision 17 (12), 5-5, 2017	59	2017
DeepGaze III: Modeling free-viewing human scanpaths with deep learning M Kümmerer, M Bethge, TSA Wallis Journal of Vision 22 (5), 7-7, 2022	58	2022
Testing models of peripheral encoding using metamerism in an oddity paradigm TSA Wallis, M Bethge, FA Wichmann Journal of vision 16 (2), 4-4, 2016	55	2016
Image content is more important than Bouma’s Law for scene metamers TSA Wallis, CM Funke, AS Ecker, LA Gatys, FA Wichmann, M Bethge ELife 8, e42512, 2019	53*	2019
Staying focused: A functional account of perceptual suppression during binocular rivalry DH Arnold, PM Grove, TSA Wallis Journal of Vision 7 (7), 7-7, 2007	53	2007
Image correlates of crowding in natural scenes TSA Wallis, PJ Bex Journal of Vision 12 (7), 6: 1--19, 2012	50	2012
Motion-induced blindness and motion streak suppression TSA Wallis, DH Arnold Current Biology 19 (4), 325-329, 2009	45	2009
Visual crowding is correlated with awareness TSA Wallis, PJ Bex Current Biology 21 (3), 254-258, 2011	42	2011
Exemplary natural images explain CNN activations better than state-of-the-art feature visualization J Borowski, RS Zimmermann, J Schepers, R Geirhos, TSA Wallis, ... arXiv preprint arXiv:2010.12606, 2020	41	2020
Binocular switch suppression: A new method for persistently rendering the visible ‘invisible’ DH Arnold, P Law, TSA Wallis Vision research 48 (8), 994-1001, 2008	38	2008
How well do feature visualizations support causal understanding of CNN activations? RS Zimmermann, J Borowski, R Geirhos, M Bethge, T Wallis, W Brendel Advances in Neural Information Processing Systems 34, 11730-11744, 2021	30	2021
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations MA Pedziwiatr, M Kümmerer, TSA Wallis, M Bethge, C Teufel Cognition 206, 104465, 2021	28	2021
Motion-induced blindness is not tuned to retinal speed TSA Wallis, DH Arnold Journal of Vision 8 (2), 11-11, 2008	25	2008
The notorious difficulty of comparing human and machine perception CM Funke, J Borowski, K Stosio, W Brendel, TS Wallis, M Bethge arXiv preprint arXiv:2004.09406 20, 2020	24	2020
Perceived size and spatial coding DH Arnold, A Birt, TSA Wallis Journal of Neuroscience 28 (23), 5954-5958, 2008	20	2008

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