| Flownet: Learning optical flow with convolutional networks A Dosovitskiy, P Fischer, E Ilg, P Hausser, C Hazirbas, V Golkov, ... International Conference on Computer Vision (ICCV), 2758-2766, 2015 | 2477* | 2015 |
| Image-based localization using LSTMs for structured feature correlation F Walch, C Hazirbas, L Leal-Taixé, T Sattler, S Hilsenbeck, D Cremers International Conference on Computer Vision (ICCV), 2017 | 311 | 2017 |
| FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture C Hazirbas, L Ma, C Domokos, D Cremers Asian Conference on Computer Vision (ACCV), 2016 | 304 | 2016 |
| Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems T Meinhardt, M Möller, C Hazirbas, D Cremers International Conference on Computer Vision (ICCV), 2017 | 172 | 2017 |
| 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, T Brox International Journal of Computer Vision (IJCV), 1-19, 2018 | 105 | 2018 |
| CAPTCHA Recognition with Active Deep Learning F Stark, C Hazirbas, R Triebel, D Cremers German Conference on Pattern Recognition Workshop (GCPRW), 94, 2015 | 91 | 2015 |
| Deep depth from focus C Hazirbas, SG Soyer, MC Staab, L Leal-Taixé, D Cremers Asian Conference on Computer Vision (ACCV), 2018 | 34 | 2018 |
| Interactive Multi-label Segmentation of RGB-D Images J Diebold, N Demmel, C Hazirbas, M Möller, D Cremers Scale Space and Variational Methods in Computer Vision (SSVM), 294-306, 2015 | 13 | 2015 |
| Deep Learning for Image-Based Localization F Walch, D Cremers, S Hilsenbeck, C Hazirbas, L Leal-Taix Master’s thesis, 2016 | 7 | 2016 |
| Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation C Hazirbas, J Diebold, D Cremers Scale Space and Variational Methods in Computer Vision (SSVM) 9087, 243-255, 2015 | 5* | 2015 |
| Image processing using a convolutional neural network C Schroers, F Perazzi, C Hazirbas US Patent 10,706,503, 2020 | | 2020 |
| Learning Geometry and Semantics for Deep Image Restoration C Hazırbaş Technische Universität München, 2019 | | 2019 |
| TUM RGB-D Scribble-based Segmentation Benchmark C Hazirbas, A Wiedemann, R Maier, L Leal-Taixe, D Cremers https://github.com/tum-vision/rgbd_scribble_benchmark, 2018 | | 2018 |
Daniel CremersTechnical University of MunichVerified email at tum.de
