From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

Author:

Singer Johannes J. D.123,Seeliger Katja14,Kietzmann Tim C.56,Hebart Martin N.17

Affiliation:

1. Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

2. Department of Psychology, Ludwig Maximilian University, Munich, Germany

3. johannes.singer@arcor.de

4. kseeliger@posteo.jp

5. Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands

6. t.kietzmann@donders.ru.nl

7. hebart@cbs.mpg.de

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

Subject

Sensory Systems,Ophthalmology

Reference55 articles.

1. Deep convolutional networks do not classify based on global object shape;Baker;PLOS Computational Biology,,2018

2. Local features and global shape information in object classification by deep convolutional neural networks;Baker;Vision Research,,2020

3. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing;Benjamini;Journal of the Royal Statistical Society. Series B (Methodological),,1995

4. Surface versus edge-based determinants of visual recognition;Biederman;Cognitive Psychology,,1988

5. Object-scene conceptual regularities reveal fundamental differences between biological and artificial object vision;Bracci;bioRxiv Preprint,2021

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