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Deep Learning

Contact: Dr. Laura Leal-Taixe, Caner Hazırbaş, Philip Häusser, Vladimir Golkov, Lingni Ma

Deep Learning is a powerful machine learning tool that showed outstanding performance in many fields. One of the greatest successes of Deep Learning has been achieved in large scale object recognition with Convolutional Neural Networks (CNNs). CNNs' main power comes from learning data representations directly from data in a hierarchical layer based structure.

We apply Convolutional Neural Networks in order to solve computer vision tasks such as optical flow, scene understanding, and develop state-of-the-art methods.

Deep Depth From Focus

DDFF aims at predicting a depth map from a given focal stack in which the focus of the camera gradually changes. DDFFNet is an end-to-end trained Convolutional Neural Network, designed to solve the highly ill-posed depth from focus task. Please visit the DDFF Project Page for details.


In our recent ICCV'15 paper, we presented two CNN architectures to estimate the optical flow given one image pair. We train the network end-to-end on a GPU. Our system works as good as state-of-the-art techniques.

Journal Articles
q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (V. Golkov, A. Dosovitskiy, J. I. Sperl, M. I. Menzel, M. Czisch, P. Sämann, T. Brox, D. Cremers), In IEEE Transactions on Medical Imaging, volume 35, 2016. [bib] [pdf]Special Issue on Deep Learning
Conference and Workshop Papers
One-Shot Video Object Segmentation (S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, L. Van Gool), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [bib] [pdf]
Deep Depth From Focus (Caner Hazirbas, Laura Leal-Taixé, Daniel Cremers), In ArXiv preprint arXiv:1704.01085, 2017. ([arxiv]) [bib]
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (Tim Meinhardt, Michael Möller, Caner Hazirbas, Daniel Cremers), In ArXiv preprint arXiv:1704.03488, 2017. ([arxiv]) [bib]
3D Deep Learning for Biological Function Prediction from Physical Fields (V. Golkov, M. J. Skwark, A. Mirchev, G. Dikov, A. R. Geanes, J. Mendenhall, J. Meiler, D. Cremers), In ArXiv preprint, 2017. (arXiv:1704.04039) [bib] [pdf]
Image-based localization using LSTMs for structured feature correlation (Florian Walch, Caner Hazirbas, Laura Leal-Taixé, Torsten Sattler, Sebastian Hilsenbeck, Daniel Cremers), In ArXiv preprint 1611.07890, 2016. ([arxiv]) [bib]
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture (C. Hazirbas, L. Ma, C. Domokos, D. Cremers), In Asian Conference on Computer Vision, 2016. ([code]) [bib] [pdf]
Protein Contact Prediction from Amino Acid Co-Evolution Using Convolutional Networks for Graph-Valued Images (V. Golkov, M. J. Skwark, A. Golkov, A. Dosovitskiy, T. Brox, J. Meiler, D. Cremers), In Annual Conference on Neural Information Processing Systems (NIPS), 2016. ([video]) [bib] [pdf]Oral Presentation (acceptance rate: under 2%)
CAPTCHA Recognition with Active Deep Learning (F. Stark, C. Hazirbas, R. Triebel, D. Cremers), In GCPR Workshop on New Challenges in Neural Computation, 2015. ([code]) [bib] [pdf]
FlowNet: Learning Optical Flow with Convolutional Networks (A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video],[code]) [bib] [pdf] [doi]
q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (V. Golkov, A. Dosovitskiy, P. Sämann, J. I. Sperl, T. Sprenger, M. Czisch, M. I. Menzel, P. A. Gómez, A. Haase, T. Brox, D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015. [bib] [pdf]
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Last edited 06.04.2017 18:16 by Caner Hazirbas