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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich

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My personal webpage: hazirbas.com

Google Scholar

: i10-index: 8, h-index: 8, citations: 3996

Publications


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Journal Articles
2018
[]What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? (N Mayer, E Ilg, P Fischer, C Hazirbas, D Cremers, A Dosovitskiy and T Brox), In , volume 41, 2018. (arxiv) [bibtex] [arXiv:1801.06397]
Conference and Workshop Papers
2018
[]Deep Depth From Focus (C. Hazirbas, S. G. Soyer, M. C. Staab, L. Leal-Taixé and D. Cremers), In Asian Conference on Computer Vision (ACCV), 2018. ([arxiv], Deep Depth From Focus,[dataset]) [bibtex]
2017
[]Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (T. Meinhardt, M. Moeller, C. Hazirbas and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([arxiv], [code]) [bibtex]
[]Image-based localization using LSTMs for structured feature correlation (F. Walch, C. Hazirbas, L. Leal-Taixé, T. Sattler, S. Hilsenbeck and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([arxiv]) [bibtex]
2016
[]FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture (C. Hazirbas, L. Ma, C. Domokos and D. Cremers), In Asian Conference on Computer Vision, 2016. ([code]) [bibtex] [pdf]
2015
[]CAPTCHA Recognition with Active Deep Learning (F. Stark, C. Hazirbas, R. Triebel and D. Cremers), In GCPR Workshop on New Challenges in Neural Computation, 2015. ([code]) [bibtex] [pdf]
[]FlowNet: Learning Optical Flow with Convolutional Networks (A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers and T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video],[code]) [bibtex] [doi] [pdf]
[]Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (C. Hazirbas, J. Diebold and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]Oral Presentation
[]Interactive Multi-label Segmentation of RGB-D Images (J. Diebold, N. Demmel, C. Hazirbas, M. Möller and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]
Other Publications
2014
[]Feature Selection and Learning for Semantic Segmentation (C Hazirbas), Master's thesis, Technical University Munich, 2014.  [bibtex] [pdf]
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Informatik IX
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News

04.06.2021

Bernt Schiele (Max Planck Institute for Informatics) will give a talk in the TUM AI lecture series on June 10th, 3pm! Livestream

05.05.2021
French-German Machine Learning Symposium

French-German Machine Learning Symposium

The French-German Machine Learning Symposium aims to strengthen interactions and inspire collaborations between both countries. We invited some of the leading ML researchers from France and Germany to this two-day symposium to give a glimpse into their research, and engage in discussions on the future of machine learning and how to strengthen research collaborations in ML between France and Germany.

The list of speakers includes Yann LeCun, Cordelia Schmid, Jean-Bernard Lasserre, Bernhard Schölkopf, and many more! For the full program please visit the webpage.

03.05.2021

Ron Kimmel (Technion - Israel Institute of Technology) will give a talk in the TUM AI lecture series on May 6th, 3pm! Livestream

23.04.2021

4Seasons Dataset: We have released a novel dataset for benchmarking multi-weather SLAM in autonomous driving.

19.04.2021

Hao Li (Pinscreen) will give a talk in the TUM AI lecture series on April 22nd, 8pm! Livestream

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