I will be presenting our work on adversarial robustness at ICML'19 and CVPR'19 in Long Beach beginning next week!



David Stutz, Matthias Hein, Bernt Schiele.
Disentangling Adversarial Robustness and Generalization.
ArXiv, 2018.
[ArXiv | BibTeX | Project Page]

David Stutz, Andreas Geiger.
Learning 3D Shape Completion under Weak Supervision.
International Journal of Computer Vision, 2018.
[DOI | 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]


Articles and project pages related to the publications listed above. Also see Projects for an overview as well as THESES and SEMINAR PAPERS .


A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.

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GCPR’15 Paper “Superpixel Segmentation: An Evaluation”

After completing my bachelor thesis, I was encouraged to submit the results at the Young Researcher Forum of the German Conference on Pattern Recognition (GCPR) 2015. In this article, I want to share the paper as well as the corresponding poster.

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A comparison of several state-of-the-art superpixel algorithms using an extended version of the Berkeley Segmentation Benchmark.

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