This is an old revision of the document!
% generated by bibtexbrowser
%
% Encoding: UTF-8
@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 = {{FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture}},
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},
}