J. Borowski, R. S. Zimmermann, J. Schepers, R. Geirhos, T. S. A. Wallis, M. Bethge, and W. Brendel
Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations
arXiv, 2020
Code, URL, BibTex

C. M. Funke, J. Borowski, K. Stosio, W. Brendel, T. S. A. Wallis, and M. Bethge
The Notorious Difficulty of Comparing Human and Machine Perception
arXiv (superseded by "Five Points to Check when Comparing Visual Perception in Humans and Machines"), 2020
Code, URL, BibTex

L. A. Gatys, M. Kümmerer, T. S. A. Wallis, and M. Bethge
Guiding human gaze with convolutional neural networks
arXiv, 2017
#image manipulation, #saliency, #convolutional neural networks
URL, Supplement, BibTex

C. M. Funke, J. Borowski, K. Stosio, W. Brendel, T. S. A. Wallis, and M. Bethge
Five Points to Check when Comparing Visual Perception in Humans and Machines
Journal of Vision, 2021
Code, URL, BibTex

T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, and M. Bethge
Image content is more important than Bouma's Law for scene metamers
ELife, 2019
URL, DOI, BibTex

T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, and M. Bethge
A Parametric Texture Model Based on Deep Convolutional Features Closely Matches Texture Appearance for Humans
Journal of Vision, 17(12), 2017
#visual textures, #style transfer, #perceptual image synthesis, #cnns, #psychophysics, #appearance
Code, URL, DOI, Stimuli, Preprint, BibTex

T. S. A. Wallis, S. Tobias, M. Bethge, and F. A. Wichmann
Detecting Distortions of Peripherally Presented Letter Stimuli under Crowded Conditions
Attention, Perception & Psychophysics, 2017
#psychophysics, #letters, #spatial distortions, #crowding
URL, DOI, BibTex

T. S. A. Wallis, M. Bethge, and F. A. Wichmann
Testing models of peripheral encoding using metamerism in an oddity paradigm
Journal of Vision, 16(2), 2016
#psychophysics, #metamers, #crowding, #image appearance, #scene appearance, #blur
Code, URL, DOI, BibTex

M. Kuemmerer, T. Wallis, and M. Bethge
Information-theoretic model comparison unifies saliency metrics
Proceedings of the National Academy of Science, 112(52), 16054-16059, 2015
URL, DOI, BibTex

M. Kümmerer, T. Wallis, and M. Bethge
How close are we to understanding image-based saliency?
arXiv, 2014
#saliency
URL, BibTex

M. Kümmerer, T. S. A. Wallis, and M. Bethge
Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics
The European Conference on Computer Vision (ECCV), 2018
URL, PDF, BibTex

M. Kümmerer, T. S. Wallis, L. A. Gatys, and M. Bethge
Understanding Low- and High-Level Contributions to Fixation Prediction
The IEEE International Conference on Computer Vision (ICCV), 2017
Code, URL, PDF, model webservice, BibTex

J. Borowski, C. M. Funke, K. Stosio, W. Brendel, T. S. A. Wallis, and M. Bethge
The Notorious Difficulty of Comparing Human and Machine Perception
NeurIPS Workshop: Shared Visual Representations in Human & Machine Intelligence (Best Paper Award), 2019
URL, BibTex

J. Borowski, C. M. Funke, K. Stosio, W. Brendel, T. S. A. Wallis, and M. Bethge
The Notorious Difficulty of Comparing Human and Machine Perception
Conference on Cognitive Computational Neuroscience, 2019
DOI, BibTex

C. Funke, J. Borowski, T. Wallis, W. Brendel, A. Ecker, and M. Bethge
Comparing the ability of humans and DNNs to recognise closed contours in cluttered images
Journal of Vision, 18(10), 2018
DOI, BibTex