Thomas S.A. Wallis

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Matthias BethgeTübingen University & Amazon ScholarVerified email at bethgelab.org
Matthias KümmererCentre for integrative Neuroscience, 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
Derek H. ArnoldSchool of Psychology, The University of QueenslandVerified email at psy.uq.edu.au
Alexander S. EckerUniversity of Göttingen, GermanyVerified email at cs.uni-goettingen.de
Judy BorowskiPhD student, University of Tübingen & International Max Planck Research School for Intelligent SystemsVerified email at bethgelab.org
Wieland BrendelEmmy Noether Fellow, University of TübingenVerified email at uni-tuebingen.de
Mark HorswillThe University of QueenslandVerified email at psy.uq.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
Roland Simon ZimmermannUniversity of TübingenVerified email at uni-tuebingen.de
Robert GeirhosUniversity of Tübingen · International Max Planck Research School for Intelligent Systems (IMPRS-IS)Verified email at uni-tuebingen.de
Warrick RoseboomDepartment of Informatics and School of Psychology, University of SussexVerified email at sussex.ac.uk
Holly E. ErskineQueensland Centre for Mental Health Research; School of Population Health, University of QueenslandVerified email at qcmhr.uq.edu.au
Luis Andres LesmesPresident, Co-Founder Adaptive Sensory TechnologyVerified email at adaptivesensorytech.com
Claudio MichaelisPhD student, University of TübingenVerified email at uni-tuebingen.de

Thomas S.A. Wallis

Institute of Psychology and Center 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	363*	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	209	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	131	2015
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	48	2007
Motion-induced blindness and motion streak suppression TSA Wallis, DH Arnold Current Biology 19 (4), 325-329, 2009	47	2009
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	45	2017
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	44	2018
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	44	2016
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	38*	2021
Visual crowding is correlated with awareness TSA Wallis, PJ Bex Current Biology 21 (3), 254-258, 2011	36	2011
Image correlates of crowding in natural scenes TSA Wallis, PJ Bex Journal of Vision 12 (7), 6: 1--19, 2012	35	2012
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	34	2008
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	29	2019
Motion-induced blindness is not tuned to retinal speed TSA Wallis, DH Arnold Journal of Vision 8 (2), 11-11, 2008	25	2008
Perceived size and spatial coding DH Arnold, A Birt, TSA Wallis Journal of Neuroscience 28 (23), 5954-5958, 2008	20	2008
Characterization of field loss based on microperimetry is predictive of face recognition difficulties TSA Wallis, CP Taylor, J Wallis, ML Jackson, PJ Bex Investigative ophthalmology & visual science 55 (1), 142-153, 2014	13	2014
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	10	2021
Guiding human gaze with convolutional neural networks LA Gatys, M Kümmerer, TSA Wallis, M Bethge arXiv preprint arXiv:1712.06492, 2017	9	2017
Spatiotemporal rivalry: A perceptual conflict involving illusory moving and static forms DH Arnold, HE Erskine, W Roseboom, TSA Wallis Psychological Science 21 (5), 692-699, 2010	8	2010
Saliency benchmarking: Separating models, maps and metrics M Kümmerer, TS Wallis, M Bethge arXiv preprint arXiv:1704.08615, 2017	7	2017

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