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@inproceedings{diebold-et-al-ssvm15,
 author = {J. Diebold and N. Demmel and C. Hazirbas and M. Möller and D. Cremers},
 title = {Interactive Multi-label Segmentation of {RGB-D} Images},
 booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM)},
 year = {2015},
 month = {june},
 keywords = {diebold, segmentation},
 doi = {10.1007/978-3-319-18461-6_24},
}

@inproceedings{hazirbas-et-al-ssvm15,
 author = {C. Hazirbas and J. Diebold and D. Cremers},
 title = {Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation},
 booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM)},
 year = {2015},
 month = {june},
 keywords = {diebold, segmentation},
 doi = {10.1007/978-3-319-18461-6_20},
 award = {Oral Presentation},
}

@mastersthesis{hazirbas2014msc,
 author = {C Hazirbas},
 title = {Feature Selection and Learning for Semantic Segmentation},
 school = {Technical University Munich},
 address = {Germany},
 year = {2014},
 month = {June},
 keywords = {feature selection, semantic segmentation, variational image segmentation, student-project},
}

@inproceedings{flownet-iccv-15,
 author = {A. Dosovitskiy and P. Fischer and E. Ilg and P. Haeusser and C. Hazirbas and V. Golkov and P. van der Smagt and D. Cremers and T. Brox},
 title = {{FlowNet: Learning Optical Flow with Convolutional Networks}},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 keywords = {deep learning, optical-flow},
 year = {2015},
 month = {dec},
 doi = {10.1109/ICCV.2015.316},
}

@inproceedings{stark-gcpr15,
 author = {F. Stark and C. Hazirbas and R. Triebel and D. Cremers},
 title = {CAPTCHA Recognition with Active Deep Learning},
 booktitle = {GCPR Workshop on New Challenges in Neural Computation},
 year = {2015},
 address = {Aachen, Germany},
 keywords = {deep learning},
}

@inproceedings{hazirbasma2016fusenet,
 author = {C. Hazirbas and L. Ma and C. Domokos and D. Cremers},
 title = {{<a href="https://hazirbas.github.io/projects/fusenet/" target="_blank">FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture</a>}},
 booktitle = {Asian Conference on Computer Vision},
 year = {2016},
 month = {november},
 keywords = {segmentation, deep learning},
}

@inproceedings{walch16spatialstms,
 author = {F. Walch and C. Hazirbas and L. Leal-Taixé and T. Sattler and S. Hilsenbeck and D. Cremers},
 title = {Image-based localization using LSTMs for structured feature correlation},
 month = {October},
 year = {2017},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 keywords = {deep learning},
}

@inproceedings{meinhardt17learning,
 author = {T. Meinhardt and M. Moeller and C. Hazirbas and D. Cremers},
 title = {Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems},
 month = {October},
 year = {2017},
 booktitle = {IEEE International Conference on Computer Vision (ICCV)},
 keywords = {deep learning},
}

@inproceedings{hazirbas18ddff,
 author = {C. Hazirbas and S. G. Soyer and M. C. Staab and L. Leal-Taixé and D. Cremers},
 title = {{Deep Depth From Focus}},
 year = {2018},
 month = {December},
 booktitle = {Asian Conference on Computer Vision (ACCV)},
 keywords = {deep learning},
}

@article{mayer18synthetic,
 author = {N Mayer and E Ilg and P Fischer and C Hazirbas and D Cremers and A Dosovitskiy and T Brox},
 title = {What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?},
 booktitle = {International Journal of Computer Vision},
 volume = {41},
 number = {8},
 pages = {1797--1812},
 year = {2018},
 month = {September},
 eprint = {arXiv:1801.06397},
 keywords = {deep learning, optical-flow},
}