Caner Hazirbas
Caner Hazirbas
Apple Inc.
Bestätigte E-Mail-Adresse bei hazirbas.com - Startseite
TitelZitiert vonJahr
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
1501*2015
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
1862016
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
174*2017
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
952017
CAPTCHA Recognition with Active Deep Learning
F Stark, C Hazirbas, R Triebel, D Cremers
German Conference on Pattern Recognition Workshop (GCPRW), 94, 2015
582015
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
552018
Deep depth from focus
C Hazirbas, SG Soyer, MC Staab, L Leal-Taixé, D Cremers
Asian Conference on Computer Vision (ACCV), 2018
182018
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
112015
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 App. 15/919,715, 2019
2019
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
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