David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele.
On Mitigating Random and Adversarial Bit Errors.
ArXiv, 2020.
[ArXiv | BibTeX | Project Page]
David Stutz, Matthias Hein, Bernt Schiele.
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks.
ICML, 2020.
[ArXiv | BibTeX | Project Page]
David Stutz, Andreas Geiger.
Learning 3D Shape Completion under Weak Supervision.
International Journal of Computer Vision, 2020.
[DOI | ArXiv | BibTeX | Project Page]
David Stutz, Matthias Hein, Bernt Schiele.
Disentangling Adversarial Robustness and Generalization.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[ArXiv | BibTeX | Project Page]
David Stutz, Andreas Geiger.
Learning 3D Shape Completion from Laser Scan Data with Weak Supervision.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[PDF | BibTeX | Project Page]
David Stutz, Alexander Hermans, Bastian Leibe.
Superpixels: an evaluation of the state-of-the-art.
Computer Vision and Image Understanding, Volume 166, 2018.
[DOI | ArXiv | PDF | BibTeX | Project Page]
David Stutz.
Superpixel segmentation: an evaluation.
German Conference on Pattern Recognition, 2015.
[PDF | BibTeX | Project Page]
Disentangling the relationship between adversarial robustness and generalization.
To date, it is unclear whether we can obtain both accurate and robust deep networks — meaning deep networks that generalize well and resist adversarial examples. In this pre-print, we aim to disentangle the relationship between adversarial robustness and generalization. The paper is available on ArXiv.
Our CVPR’18 follow-up paper has been accepted at IJCV. In this longer paper we extend our weakly-supervised 3D shape completion approach to obtain high-quality shape predictions, and also present updated, synthetic benchmarks on ShapeNet and ModelNet. The paper is available through Springer Link and ArXiv.
In this follow-up on our CVPR’18 work, we extend our weakly-supervised 3D shape completion approach to obtain high-quality shape predictions, and also present updated, synthetic benchmarks on ShapeNet and ModelNet. The paper is now available as pre-print on ArXiv. Abstract, some experimental results and a comparison to our CVPR’18 work can be found in this article.
Learning 3D shape completion under weak supervision; on ShapeNet, ModelNet, KITTI and Kinect data; published at CVPR and on ArXiv.
In this CVPR’18 paper, based on my master thesis, we propose a weakly-supervised and learning-based approach to 3D shape completion of sparse and noisy point clouds. We show that, using a learned shape prior, shape completion can be learned without access to ground truth shapes — only by knowing the object category at hand. This article provides the paper and its supplementary material.
Weakly-supervised shape completion of cars on KITTI using variational auto-encoders; including two synthetic ShapeNet-based benchmark datasets.
Revised C++ implementations of two popular superpixel algorithms, SEEDS and FH, which are shown to outperform the original implementations.
In the course of the last couple of semesters, I extended the initial comparison of superpixel algorithms in my bachelor thesis to a comprehensive comparison of 28 state-of-the-art algorithms on 5 datasets with regard to quantitative and qualitative performance. The results are now available on ArXiv.
A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.