CRC 1233 “Robust Vision”
The collaborative research center “Robust Vision – Inference Principles and Neural Mechanisms” (CRC 1233) deals with basic principles of biological and machine vision, and is a close collaboration between scientists from the University and the Max Planck Institute for Intelligent Systems. Human visual perception is amazingly robust: Even in highly variable environments, we are able to make reliable inferences about the spatial arrangement of the world from limited visual information. To achieve this, our brain must perform complex computations. Artificial vision systems, in turn – as used, for example, in self-driving cars – are making steep progress in reproducing the visual skills of humans. The goal of this centre will be to better understand the principles and algorithms that enable robust visual inference both in humans and machines.
Topics, aims and projects
Aim 1a: Generative and causal modeling
Project 2 (Lensch)
Robust material inference
Project 4 (Wichmann, Schölkopf, Bethge)
Causal inference strategies in human vision
Project 17 (Akata, Geiger)
Learning explainable policies for self-driving cars from little data
Aim 1b: Feedback and neural representations
Aim 2: Dynamic input
Project 9 (Bartels, Black)
Natural dynamic scene processing in the human brain
Project 10 (Busse, Euler)
Natural stimuli for mice: environment statistics and neural representations in the early visual system
Project 11 (Hafed, Franke)
Impacts of eye movements on visual processing: from retina to perception
Project T01 (Wahl)
Physiologically inspired robust electro-optical autofocals
Aim 3: Precortical image transformations
Projekt 12 (Euler, Bethge)
Image processing within a locally complete retinal ganglion cell population
Projekt 13 (Busse, Berens)
Visual processing of feedforward and feedback signals in the dLGN
Projekt 14 (Stingl, Schwarz)
Retinal Disease Models as a Tool for Understanding Robust Vision
Supportive Projects
Project INF (Infrastructure) (Sinz, Berens)
A collaborative data management platform for reproducible neuroscience and machine learning
Speakers
Prof. Dr. Matthias Bethge
(spokesperson)
Tübingen AI Center
University of Tübingen
Maria-von-Linden-Straße 6
D-72076 Tübingen
+49 7071 29-70862
matthias.bethge @uni-tuebingen.de
Michael Black, Ph.D., Director (Vice-Spokesperson)
Tübingen AI Center
Max Planck Institute for Intelligent Systems
Max-Planck-Ring 4
D-72076 Tübingen
+49 7071 601-1801
black @tue.mpg.de