T. Meinhardt, M. Möller, C. Hazirbas, and D. Cremers, “Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems,” in ICCV, October 2017.
bib | arXiv ]
F. Walch, C. Hazirbas, L. Leal-Taixé, T. Sattler, S. Hilsenbeck, and D. Cremers, “Image-based localization using LSTMs for structured feature correlation,” in ICCV, October 2017.
bib | arXiv ]
C. Hazirbas, L. Leal-Taixé, and D. Cremers, “Deep Depth From Focus,” in ArXiv, April 2017.
bib | arXiv ]
C. Hazirbas, L. Ma, C. Domokos, and D. Cremers, “FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture,” in ACCV, November 2016.
bib | doi | source ]
A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox, “FlowNet: Learning Optical Flow with Convolutional Networks,” in ICCV, December 2015.
bib | arXiv | doi ]
F. Stark, C. Hazirbas, R. Triebel, and D. Cremers, “CAPTCHA Recognition with Active Deep Learning,” in GCPR Workshop on New Challenges in Neural Computation, October 2015.
bib | source ]
C. Hazirbas, J. Diebold, and D. Cremers, “Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation,” in SSVM, June 2015. Oral Presentation.
bib | doi | source ]
J. Diebold, N. Demmel, C. Hazirbas, M. Möller, and D. Cremers, “Interactive Multi-label Segmentation of RGB-D Images,” in SSVM, June 2015.
bib | doi | source ]
C. Hazirbas, “Feature Selection and Learning for Semantic Segmentation,” Master's thesis, Technical University Munich, Germany, June 2014.
bib ]