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Technical University of Munich

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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CVG Group DVL Group

News

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

24.07.2020

Our practical course "Vision-based Navigation" (WS18, SS19) by Dr. Vladyslav Usenko and Nikolaus Demmel was honored as best practical course in the academic year 2018/2019 by the department for Informatics.

07.05.2020

We are organizing a workshop on Map-based Localization for Autonomous Driving at ECCV 2020, Glasgow, UK.

13.04.2020

Daniel Cremers received an ERC Advanced Grant (3.5 Mio Euro) for pioneering frontier research from the European Research Council. This constitutes his fifth ERC grant.

More


Deep Learning

Deep learning is a powerful machine learning framework that has shown outstanding performance in many fields. The main power of deep learning comes from learning data representations directly from data in a hierarchical layer-based structure.

We apply deep learning to computer vision, autonomous driving, biomedicine, time series data, language, and other fields, and develop novel methods. Among the advanced methods we use and develop are uncertainty quantification, processing of advanced data structures (sequences, graphs, geometry, high-dimensional data), probabilistic graphical models, reinforcement learning, active learning, domain adaptation, convolutional networks, recurrent networks, and causality inference.

Some of the works from our chair include: FlowNet, fuseNet, DVSO

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2020 2019 2018 2017 2016 2015 
2020
Preprints
[]Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions (V. Golkov, A. Becker, D. T. Plop, D. Čuturilo, N. Davoudi, J. Mendenhall, R. Moretti, J. Meiler and D. Cremers), In arXiv preprint arXiv:2007.07029, 2020.  [bibtex] [arXiv:2007.07029] [pdf]
Conference and Workshop Papers
[]D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry (N. Yang, L. von Stumberg, R. Wang and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [arXiv:2003.01060] [pdf]Oral Presentation
[]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 and D. Cremers), In International Conference on 3D Vision (3DV), 2020.  [bibtex] [arXiv:1704.04039] [pdf]
2020 2019 2018 2017 2016 2015 
2019
Journal Articles
[] Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization (F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers and D. Pfeiffer), In Scientific Reports, volume 9, 2019.  [bibtex] [pdf] [doi]
Preprints
[]Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods (L. Della Libera, V. Golkov, Y. Zhu, A. Mielke and D. Cremers), In arXiv preprint arXiv:1910.14594, 2019.  [bibtex] [arXiv:1910.14594] [pdf]
[]Learning to Evolve (J. Schuchardt, V. Golkov and D. Cremers), In arXiv preprint arXiv:1905.03389, 2019.  [bibtex] [arXiv:1905.03389] [pdf]
Conference and Workshop Papers
[]Negative-Unlabeled Learning for Diffusion MRI (P. Swazinna, V. Golkov, I. Lipp, E. Sgarlata, V. Tomassini, D. K. Jones and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2019.  [bibtex] [pdf]
[]q-Space Novelty Detection with Variational Autoencoders (A. Vasilev, V. Golkov, M. Meissner, I. Lipp, E. Sgarlata, V. Tomassini, D. K. Jones and D. Cremers), In MICCAI 2019 International Workshop on Computational Diffusion MRI, 2019.  [bibtex] [arXiv:1806.02997] [pdf]Oral Presentation
2020 2019 2018 2017 2016 2015 
2018
Journal Articles
[]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 and T Brox), In , volume 41, 2018. (arxiv) [bibtex] [arXiv:1801.06397]
Preprints
[]Clustering with Deep Learning: Taxonomy and New Methods (E. Aljalbout, V. Golkov, Y. Siddiqui, M. Strobel and D. Cremers), In arXiv preprint arXiv:1801.07648, 2018.  [bibtex] [arXiv:1801.07648]
Conference and Workshop Papers
[]Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (N. Yang, R. Wang, J. Stueckler and D. Cremers), In European Conference on Computer Vision (ECCV), 2018. ([arxiv],[supplementary],[video],[talk],[project]) [bibtex]Oral Presentation
[]Associative Deep Clustering - Training a Classification Network with no Labels (P. Haeusser, J. Plapp, V. Golkov, E. Aljalbout and D. Cremers), In Proc. of the German Conference on Pattern Recognition (GCPR), 2018.  [bibtex] [pdf]
[]Precursor microRNA Identification Using Deep Convolutional Neural Networks (B. T. Do, V. Golkov, G. E. Gürel and D. Cremers), In bioRxiv preprint, 2018. (bioRxiv:414656) [bibtex] [pdf]
[]q-Space Deep Learning for Alzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization (V. Golkov, P. Swazinna, M. M. Schmitt, Q. A. Khan, C. M. W. Tax, M. Serahlazau, F. Pasa, F. Pfeiffer, G. J. Biessels, A. Leemans and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018.  [bibtex] [pdf]
[]Deep Depth From Focus (C. Hazirbas, S. G. Soyer, M. C. Staab, L. Leal-Taixé and D. Cremers), In Asian Conference on Computer Vision (ACCV), 2018. ([arxiv], Deep Depth From Focus,[dataset]) [bibtex]
2020 2019 2018 2017 2016 2015 
2017
Preprints
[]Regularization for Deep Learning: A Taxonomy (J. Kukačka, V. Golkov and D. Cremers), In arXiv preprint arXiv:1710.10686, 2017.  [bibtex] [arXiv:1710.10686] [pdf]
Conference and Workshop Papers
[]Associative Domain Adaptation (P. Haeusser, T. Frerix, A. Mordvintsev and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
[]Better Text Understanding Through Image-To-Text Transfer (K. Kurach, S. Gelly, M. Jastrzebski, P. Haeusser, O. Teytaud, D. Vincent and O. Bousquet), In arxiv:1705.08386, 2017.  [bibtex] [pdf]
[]One-Shot Video Object Segmentation (S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers and L. V Gool), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex] [pdf]
[]Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (T. Meinhardt, M. Moeller, C. Hazirbas and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([arxiv], [code]) [bibtex]
[]Learning by Association - A versatile semi-supervised training method for neural networks (P. Haeusser, A. Mordvintsev and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
[]Establishment of an interdisciplinary workflow of machine learning-based Radiomics in sarcoma patients (J.C. Peeken, C. Knie, V. Golkov, K. Kessel, F. Pasa, Q. Khan, M. Seroglazov, J. Kukačka, T. Goldberg, L. Richter, J. Reeb, B. Rost, F. Pfeiffer, D. Cremers, F. Nüsslin and S.E. Combs), In 23. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO), 2017.  [bibtex]
[]Image-based localization using LSTMs for structured feature correlation (F. Walch, C. Hazirbas, L. Leal-Taixé, T. Sattler, S. Hilsenbeck and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([arxiv]) [bibtex]
2020 2019 2018 2017 2016 2015 
2016
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 and D. Cremers), In IEEE Transactions on Medical Imaging, volume 35, 2016. Special Issue on Deep Learning [bibtex] [pdf]Special Issue on Deep Learning
Conference and Workshop Papers
[]FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture (C. Hazirbas, L. Ma, C. Domokos and D. Cremers), In Asian Conference on Computer Vision, 2016. ([code]) [bibtex] [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 and D. Cremers), In Annual Conference on Neural Information Processing Systems (NIPS), 2016. ([video]) [bibtex] [pdf]Oral Presentation (acceptance rate: under 2%)
2020 2019 2018 2017 2016 2015 
2015
Conference and Workshop Papers
[]CAPTCHA Recognition with Active Deep Learning (F. Stark, C. Hazirbas, R. Triebel and D. Cremers), In GCPR Workshop on New Challenges in Neural Computation, 2015. ([code]) [bibtex] [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 and T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video],[code]) [bibtex] [doi] [pdf]
[]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 and D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015.  [bibtex] [pdf]
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Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
CVG Group DVL Group

News

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

24.07.2020

Our practical course "Vision-based Navigation" (WS18, SS19) by Dr. Vladyslav Usenko and Nikolaus Demmel was honored as best practical course in the academic year 2018/2019 by the department for Informatics.

07.05.2020

We are organizing a workshop on Map-based Localization for Autonomous Driving at ECCV 2020, Glasgow, UK.

13.04.2020

Daniel Cremers received an ERC Advanced Grant (3.5 Mio Euro) for pioneering frontier research from the European Research Council. This constitutes his fifth ERC grant.

More