SFB 1233:  Robust Vision - Inference Principles and Neural Mechanisms

• Subject Area: Medicine ; Computer Science, Systems and Electrical Engineering ; Social and Behavioural Sciences
• Term: since 2017
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 276693517

The Collaborative Research Centre (CRC) 1233 brings together leading researchers in machine learning, computer vision and systems neuroscience to uncover the computational principles underlying robust vision. Vision plays a key role for animals and humans to achieve a reliable correspondence between the brain's internal model of the world and the external surroundings. It relies on the sensation and interpretation of meaningful patterns implicit in the luminance signals distributed across the entire image. Understanding the principles and algorithms that facilitate robust vision of these patterns plays a fundamental role in understanding biological vision. Big strides have been made in computer vision over recent years with algorithms being tested on increasingly challenging problems, including real-world applications such as autonomous car driving. However, human vision still clearly excels in robustness. The Collaborative Research Centre (CRC) leverages the successes in computer vision and seeks to advance our understanding of the robustness of vision, both in biology and in machines. In close collaborations between computational researchers and neuroscientists, our goal is to uncover the principles of robust vision and to identify its neural basis in the mammalian brain. In particular, the CRC focuses on areas where the neurobiology of vision prominently diverges from current machine vision algorithms and studies• the computational use of feedback in the brain and how generative and causal modelling can improve the robustness of visual inference algorithms (Aim 1)• how robust visual inference is affected by the dynamics of natural image acquisition (Aim 2)• how robust visual inference is affected by pre-cortical transformations as determined from neurobiological measurements (Aim 3).The notion of robustness is tightly linked to the notion of generalisation, i.e. the ability to handle situations (or tasks) that differ from previously encountered situations. The ability to generalize allows also for task flexibility in realistic and dynamic vision problems, which will be a convergent theme of all projects in the new funding period. All projects have strongly benefited from the close and productive interdisciplinary interactions within the CRC leading to exciting new hypotheses that will be investigated in the second funding period.

• DFG Programme: Collaborative Research Centres

Current projects

• 04 - Causal inference strategies in human vision (Project Heads Bethge, Matthias ;  Schölkopf, Bernhard ;  Wichmann, Felix A. )
• 06 - Top-down control of visual inference in sensory representations in early visual cortex (Project Heads Macke, Jakob ;  Nienborg, Hendrikje ;  Sinz, Fabian ;  Wichmann, Felix A. )
• 14 - Retinal Disease Models as a Tool for Understanding Robust Vision (Project Heads Macke, Jakob ;  Schwarz, Ph.D., Christina ;  Stingl, Katarina ;  Zeck, Günther ;  Zrenner, Eberhart )
• A01 - Robust material inference (Project Heads Gehler, Peter ;  Lensch, Hendrik ;  Schölkopf, Bernhard )
• B02 - Large-scale neuronal interactions during natural vision (Project Heads Bartels, Ph.D., Andreas ;  Siegel, Markus )
• B03 - Natural dynamic scene processing in the human brain (Project Heads Bartels, Ph.D., Andreas ;  Black, Ph.D., Michael )
• C01 - Task–dependent top-down modulation of visual processing (Project Heads Dayan, Peter ;  Franz, Volker ;  von Luxburg, Ulrike )
• C02 - Impacts of eye movements on visual processing: from retina to perception (Project Heads Franke, Katrin ;  Hafed, Ph.D., Ziad ;  Schaeffel, Frank )
• D01 - Natural stimuli for mice: environment statistics and neural representations in the early visual system (Project Heads Busse, Laura ;  Euler, Thomas ;  Schaeffel, Frank )
• D02 - Image processing within a locally complete retinal ganglion cell population (Project Heads Bethge, Matthias ;  Euler, Thomas )
• D03 - Visual processing of feedforward and feedback signals in the dLGN (Project Heads Berens, Philipp ;  Busse, Laura )
• INF - A collaborative data management platform for reproducible neuroscience and machine learning (Project Heads Berens, Philipp ;  Sinz, Fabian )
• TRAT01 - Physiologically inspired robust electro-optical autofocals (Project Head Wahl, Siegfried )
• Zehem15 - Administration (Project Head Bethge, Matthias )

Completed projects

• 01 - Physics-based scene understanding (Project Heads Gehler, Peter ;  Lensch, Hendrik )
• 03 - Comparing humans and machines on robust visual inference (Project Heads Bethge, Matthias ;  Wallis, Ph.D., Thomas )
• 08 - Integration of bottom-up and top-down processing in sleep-dependent (Project Heads Nienborg, Hendrikje ;  Rauss, Karsten )
• 17 - Learning explainable policies for self-driving cars from little data (Project Heads Akata, Zeynep ;  Geiger, Andreas )

• Applicant Institution: Eberhard Karls Universität Tübingen
• Participating University: Ludwig-Maximilians-Universität München
• Participating Institution: Max-Planck-Institut für biologische Kybernetik; NMI Naturwissenschaftliches und Medizinisches Institut an der Universität Tübingen; Max-Planck-Institut für Intelligente Systeme (MPI); Standort Tübingen
• Spokesperson: Professor Dr. Matthias Bethge