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Matthias Kümmerer
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2020 – today
- 2024
- [c10]Mark Basting, Robert-Jan Bruintjes, Thaddäus Wiedemer, Matthias Kümmerer, Matthias Bethge, Jan van Gemert:
Scale Learning in Scale-Equivariant Convolutional Networks. VISIGRAPP (2): VISAPP 2024: 567-574 - [i11]Matthias Tangemann, Matthias Kümmerer, Matthias Bethge:
Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli. CoRR abs/2411.01505 (2024) - 2023
- [c9]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CLeaR 2023: 281-327 - [c8]Ori Press, Steffen Schneider, Matthias Kümmerer, Matthias Bethge:
RDumb: A simple approach that questions our progress in continual test-time adaptation. NeurIPS 2023 - [i10]Ori Press, Steffen Schneider, Matthias Kümmerer, Matthias Bethge:
RDumb: A simple approach that questions our progress in continual test-time adaptation. CoRR abs/2306.05401 (2023) - 2022
- [c7]Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge:
Disentanglement and Generalization Under Correlation Shifts. CoLLAs 2022: 116-141 - 2021
- [c6]Akis Linardos, Matthias Kümmerer, Ori Press, Matthias Bethge:
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling. ICCV 2021: 12899-12908 - [i9]Matthias Kümmerer, Matthias Bethge:
State-of-the-Art in Human Scanpath Prediction. CoRR abs/2102.12239 (2021) - [i8]Akis Linardos, Matthias Kümmerer, Ori Press, Matthias Bethge:
Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling. CoRR abs/2105.12441 (2021) - [i7]Matthias Tangemann
, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CoRR abs/2110.06562 (2021) - [i6]Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge:
Disentanglement and Generalization Under Correlation Shifts. CoRR abs/2112.14754 (2021) - 2020
- [b1]Matthias Kümmerer:
How well can we predict where people look in images? University of Tübingen, Germany, 2020 - [c5]Matthias Tangemann
Measuring the Importance of Temporal Features in Video Saliency. ECCV (28) 2020: 667-684
2010 – 2019
- 2019
- [c4]Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge:
Accurate, reliable and fast robustness evaluation. NeurIPS 2019: 12841-12851 - [i5]Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge:
Accurate, reliable and fast robustness evaluation. CoRR abs/1907.01003 (2019) - 2018
- [c3]Matthias Kümmerer
Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics. ECCV (16) 2018: 798-814 - 2017
- [c2]Matthias Kümmerer
Understanding Low- and High-Level Contributions to Fixation Prediction. ICCV 2017: 4799-4808 - [i4]Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge:
Saliency Benchmarking: Separating Models, Maps and Metrics. CoRR abs/1704.08615 (2017) - [i3]Leon A. Gatys, Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge:
Guiding human gaze with convolutional neural networks. CoRR abs/1712.06492 (2017) - 2016
- [i2]Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge:
DeepGaze II: Reading fixations from deep features trained on object recognition. CoRR abs/1610.01563 (2016) - 2015
- [c1]Matthias Kümmerer, Lucas Theis, Matthias Bethge:
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. ICLR (Workshop) 2015 - 2014
- [i1]Matthias Kümmerer, Thomas Wallis, Matthias Bethge:
How close are we to understanding image-based saliency? CoRR abs/1409.7686 (2014)
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