1 code implementation • 3 Dec 2024 • Andreas C. Schneider, Valentin Neuhaus, David A. Ehrlich, Abdullah Makkeh, Alexander S. Ecker, Viola Priesemann, Michael Wibral
In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization.
no code implementations • 21 Oct 2024 • Polina Turishcheva, Laura Hansel, Martin Ritzert, Marissa A. Weis, Alexander S. Ecker
Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines.
no code implementations • 29 Jan 2024 • Richard Vogg, Timo Lüddecke, Jonathan Henrich, Sharmita Dey, Matthias Nuske, Valentin Hassler, Derek Murphy, Julia Fischer, Julia Ostner, Oliver Schülke, Peter M. Kappeler, Claudia Fichtel, Alexander Gail, Stefan Treue, Hansjörg Scherberger, Florentin Wörgötter, Alexander S. Ecker
We start with a survey of the state-of-the-art methods for computer vision problems that are directly relevant to the video-based study of animal behavior, including object detection, multi-individual tracking, individual identification, and (inter)action recognition.
1 code implementation • 29 Nov 2023 • Max F. Burg, Thomas Zenkel, Michaela Vystrčilová, Jonathan Oesterle, Larissa Höfling, Konstantin F. Willeke, Jan Lause, Sarah Müller, Paul G. Fahey, Zhiwei Ding, Kelli Restivo, Shashwat Sridhar, Tim Gollisch, Philipp Berens, Andreas S. Tolias, Thomas Euler, Matthias Bethge, Alexander S. Ecker
Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed.
3 code implementations • 31 May 2023 • Polina Turishcheva, Paul G. Fahey, Laura Hansel, Rachel Froebe, Kayla Ponder, Michaela Vystrčilová, Konstantin F. Willeke, Mohammad Bashiri, Eric Wang, Zhiwei Ding, Andreas S. Tolias, Fabian H. Sinz, Alexander S. Ecker
We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
3 code implementations • 17 Jun 2022 • Konstantin F. Willeke, Paul G. Fahey, Mohammad Bashiri, Laura Pede, Max F. Burg, Christoph Blessing, Santiago A. Cadena, Zhiwei Ding, Konstantin-Klemens Lurz, Kayla Ponder, Taliah Muhammad, Saumil S. Patel, Alexander S. Ecker, Andreas S. Tolias, Fabian H. Sinz
The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input.
no code implementations • 23 Dec 2021 • Marissa A. Weis, Laura Hansel, Timo Lüddecke, Alexander S. Ecker
GraphDINO is a novel transformer-based representation learning method for spatially-embedded graphs.
5 code implementations • CVPR 2022 • Timo Lüddecke, Alexander S. Ecker
After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query.
Ranked #3 on
Referring Image Matting (Keyword-based)
on RefMatte
no code implementations • 9 Nov 2020 • Claudio Michaelis, Matthias Bethge, Alexander S. Ecker
We here show that this generalization gap can be nearly closed by increasing the number of object categories used during training.
1 code implementation • 12 Jun 2020 • Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker
Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding.
no code implementations • ICLR 2020 • Ivan Ustyuzhaninov, Santiago A. Cadena, Emmanouil Froudarakis, Paul G. Fahey, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Fabian H. Sinz, Andreas S. Tolias, Matthias Bethge, Alexander S. Ecker
Similar to a convolutional neural network (CNN), the mammalian retina encodes visual information into several dozen nonlinear feature maps, each formed by one ganglion cell type that tiles the visual space in an approximately shift-equivariant manner.
no code implementations • NeurIPS Workshop Neuro_AI 2019 • Santiago A. Cadena, Fabian H. Sinz, Taliah Muhammad, Emmanouil Froudarakis, Erick Cobos, Edgar Y. Walker, Jake Reimer, Matthias Bethge, Andreas Tolias, Alexander S. Ecker
Recent work on modeling neural responses in the primate visual system has benefited from deep neural networks trained on large-scale object recognition, and found a hierarchical correspondence between layers of the artificial neural network and brain areas along the ventral visual stream.
4 code implementations • 17 Jul 2019 • Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, Wieland Brendel
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
Ranked #1 on
Robust Object Detection
on MS COCO
1 code implementation • NeurIPS 2018 • Fabian Sinz, Alexander S. Ecker, Paul Fahey, Edgar Walker, Erick Cobos, Emmanouil Froudarakis, Dimitri Yatsenko, Zachary Pitkow, Jacob Reimer, Andreas Tolias
However, in many cases this approach requires that the model is able to generalize to stimulus statistics that it was not trained on, such as band-limited noise and other parameterized stimuli.
3 code implementations • 28 Nov 2018 • Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge, Alexander S. Ecker
We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult.
Ranked #1 on
One-Shot Instance Segmentation
on MS COCO
1 code implementation • ICLR 2019 • Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis, Paul G. Fahey, Santiago A. Cadena, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Andreas S. Tolias, Matthias Bethge
We present a framework to identify common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations.
1 code implementation • ECCV 2018 • Santiago A. Cadena, Marissa A. Weis, Leon A. Gatys, Matthias Bethge, Alexander S. Ecker
Here we propose a method to discover invariances in the responses of hidden layer units of deep neural networks.
1 code implementation • ICML 2018 • Claudio Michaelis, Matthias Bethge, Alexander S. Ecker
We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example.
Ranked #1 on
One-Shot Segmentation
on Cluttered Omniglot
1 code implementation • NeurIPS 2017 • David Klindt, Alexander S. Ecker, Thomas Euler, Matthias Bethge
Traditional methods for neural system identification do not capitalize on this separation of “what” and “where”.
no code implementations • NeurIPS 2017 • David A. Klindt, Alexander S. Ecker, Thomas Euler, Matthias Bethge
Our network scales well to thousands of neurons and short recordings and can be trained end-to-end.
no code implementations • 22 Feb 2017 • Christina M. Funke, Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
Here we present a parametric model for dynamic textures.
6 code implementations • CVPR 2017 • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
Neural Style Transfer has shown very exciting results enabling new forms of image manipulation.
2 code implementations • CVPR 2016 • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
Rendering the semantic content of an image in different styles is a difficult image processing task.
283 code implementations • 26 Aug 2015 • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image.
16 code implementations • NeurIPS 2015 • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition.