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.
2 code implementations • 16 Apr 2020 • Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence.
1 code implementation • 7 Dec 2019 • Michal Rolínek, Vít Musil, Anselm Paulus, Marin Vlastelica, Claudio Michaelis, Georg Martius
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models.
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
7 code implementations • ICLR 2019 • Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
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
4 code implementations • 7 Jul 2018 • Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel, Matthias Bethge
We introduce one-shot texture segmentation: the task of segmenting an input image containing multiple textures given a patch of a reference texture.
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