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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Informatik IX
Computer Vision Group

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

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18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

31.08.2022

Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.

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, anomaly detection, convolutional networks, recurrent networks, and causality inference.

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

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2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015
2024
Journal Articles
[]Multi-vehicle trajectory prediction and control at intersections using state and intention information (D Zhu, Q Khan and D Cremers), In Neurocomputing, Elsevier, 2024. ([link][project page][code][pre-print]) [bibtex] [pdf]
Conference and Workshop Papers
[]Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining (U Sahin, H Li, Q Khan, D Cremers and T Volker), In IEEE Winter Conference on Applications of Computer Vision (WACV, 2024. ([arXiv][project page][code]) [bibtex]
[]SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering (M Brahimi, B Haefner, T Yenamandra, B Goldluecke and D Cremers), In IEEE Winter Conference on Applications of Computer Vision (WACV), 2024. ([supp]) [bibtex] [arXiv:2212.04968] [pdf]
2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015
2023
Journal Articles
[]Robust Autonomous Vehicle Pursuit without Expert Steering Labels (J Pan, C Zhou, M Gladkova, Q Khan and D Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 8, 2023. ([arXiv][code]) [bibtex]
[]Learning vision based autonomous lateral vehicle control without supervision (Q Khan, I Sülö, M Öcal and D Cremers), In Applied Intelligence, Springer, 2023. ([paper][github]) [bibtex] [video]
Preprints
[]Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution (HN Dang, V Golkov, T Wimmer, D Cremers, A Maier and M Zaiss), In arXiv preprint arXiv:2305.07524, 2023.  [bibtex] [arXiv:2305.07524]
[]Scale-Equivariant Deep Learning for 3D Data (T Wimmer, V Golkov, HN Dang, M Zaiss, A Maier and D Cremers), In arXiv preprint, 2023.  [bibtex] [arXiv:2304.05864] [pdf]
[]Quality Control at Your Fingertips: Quality-Aware Translation Models (C Tomani, D Vilar, M Freitag, C Cherry, S Naskar, M Finkelstein and D Cremers), In arXiv preprint, 2023.  [bibtex] [arXiv:2310.06707]
Conference and Workshop Papers
[]CASSPR: Cross Attention Single Scan Place Recognition (Y Xia, M Gladkova, R Wang, Q Li, U Stilla, JF. Henriques and D Cremers), In IEEE International Conference on Computer Vision (ICCV), 2023. ([code]) [bibtex] [arXiv:2211.12542]
[]Multi Agent Navigation in Unconstrained Environments Using a Centralized Attention Based Graphical Neural Network Controller (Y Ma, Q Khan and D Cremers), In IEEE 26th International Conference on Intelligent Transportation Systems, 2023. ([project page][code]) [bibtex] [pdf]
[]LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels (J Schmidt, Q Khan and D Cremers), In IEEE 26th International Conference on Intelligent Transportation Systems, 2023. ([project page][arxiv][code]) [bibtex]
[]Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares (D Muhle, L Koestler, KM Jatavallabhula and D Cremers), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. ([project page]) [bibtex]
[] GPT4MR: Exploring GPT-4 as an MR Sequence and Reconstruction Programming Assistant (M Zaiss, HN Dang, V Golkov, J Rajput, D Cremers, F Knoll and A Maier), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2023.  [bibtex] [pdf]Oral Presentation
[]Behind the Scenes: Density Fields for Single View Reconstruction (F Wimbauer, N Yang, C Rupprecht and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. ([project page]) [bibtex] [arXiv:2301.07668]
[]Beyond In-Domain Scenarios: Robust Density-Aware Calibration (C Tomani, F Waseda, Y Shen and D Cremers), In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.  [bibtex] [arXiv:2302.05118]
2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015
2022
Journal Articles
[] Deep Learning in Attosecond Metrology (C. Brunner, A. Duensing, C. Schröder, M. Mittermair, V. Golkov, M. Pollanka, D. Cremers and R. Kienberger), In Optics Express, OSA, volume 30, 2022.  [bibtex] [pdf] [doi]Editor's Pick
Preprints
[]Challenger: Training with Attribution Maps (C Tomani and D Cremers), In arXiv preprint, 2022.  [bibtex] [arXiv:2205.15094] [pdf]
Conference and Workshop Papers
[]A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs (HHH Hsu, Y Shen and D Cremers), In NeurIPS 2022 Workshop: New Frontiers in Graph Learning, 2022. ([code]) [bibtex] [arXiv:2210.15575]
[]Deep Combinatorial Aggregation (Y Shen and D Cremers), In NeurIPS, 2022. ([code]) [bibtex] [arXiv:2210.06436]
[]What Makes Graph Neural Networks Miscalibrated? (HHH Hsu, Y Shen, C Tomani and D Cremers), In NeurIPS, 2022. ([code]) [bibtex] [arXiv:2210.06391]
[]Ventriloquist-Net: Leveraging Speech Cues for Emotive Talking Head Generation (D Das, Q Khan and D Cremers), In IEEE International Conference on Image Processing, 2022.  [bibtex]
[]Biologically Inspired Neural Path Finding (L Hang, Q Khan, V Tresp and D Cremers), In Brain Informatics, Springer International Publishing, 2022. ([code]) [bibtex]
[]Lateral Ego-Vehicle Control Without Supervision Using Point Clouds (F Müller, Q Khan and D Cremers), In Pattern Recognition and Artificial Intelligence, Springer International Publishing, 2022.  [bibtex] [pdf]
[]Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration (C Tomani, D Cremers and F Buettner), In European Conference on Computer Vision (ECCV), 2022.  [bibtex] [arXiv:2102.12182]
2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015
2021
Preprints
[]Scene Graph Generation for Better Image Captioning? (M. Mozes, M. Schmitt, V. Golkov, H. Schütze and D. Cremers), In arXiv preprint, 2021.  [bibtex] [arXiv:2109.11398] [pdf]
[]Rotation-Equivariant Deep Learning for Diffusion MRI (P. Müller, V. Golkov, V. Tomassini and D. Cremers), In arXiv preprint, 2021.  [bibtex] [arXiv:2102.06942] [pdf]
Conference and Workshop Papers
[]Explicit pairwise factorized graph neural network for semi-supervised node classification (Y Wang, Y Shen and D Cremers), In UAI, 2021. ([code]) [bibtex] [arXiv:2107.13059]
[]Kronecker-Factored Optimal Curvature (D Schnaus, J Lee and R Triebel), In Bayesian Deep Learning NeurIPS 2021 Workshop, 2021. ([poster]) [bibtex] [pdf]
[]Post-hoc Uncertainty Calibration for Domain Drift Scenarios (C Tomani, S Gruber, ME Erdem, D Cremers and F Buettner), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.  [bibtex] [arXiv:2012.10988]Oral Presentation
[]Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration (C Tomani and F Buettner), In InThirty-FifthAAAIConferenceonArtificialIntelligence(AAAI-2021), 2021.  [bibtex] [arXiv:2012.10923]
[]Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning (P. Wenzel, T. Schön, L. Leal-Taixé and D. Cremers), In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021. ([arXiv]) [bibtex]
[]SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition (Y. Xia, Y. Xu, S. Li, R. Wang, J. Du, D. Cremers and U. Stilla), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([arxiv]) [bibtex]Oral Presentation
[]Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry (Q. Khan, P. Wenzel and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. ([arXiv]) [bibtex]
[]Rotation-Equivariant Deep Learning for Diffusion MRI (short version) (P. Müller, V. Golkov, V. Tomassini and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2021.  [bibtex] [pdf]
[]MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([project page]) [bibtex] [arXiv:2011.11814]
PhD Thesis
[]Deep learning and variational analysis for high-dimensional and geometric biomedical data (V. Golkov), PhD thesis, Department of Informatics, Technical University of Munich, Germany, 2021.  [bibtex] [pdf]
2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015
2020
Journal Articles
[]GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization (L. von Stumberg, P. Wenzel, Q. Khan and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 5, 2020. ([arXiv][video][project page][supplementary]) [bibtex]
Preprints
[]Neural Online Graph Exploration (I Chiotellis and D Cremers), In arXiv preprint arXiv:2012.03345, 2020. ([arxiv]) [bibtex]
[]Speech Synthesis and Control Using Differentiable DSP (G Fabbro, V Golkov, T Kemp and D Cremers), In arXiv preprint arXiv:2010.15084, 2020. ([listen to audio results]) [bibtex] [arXiv:2010.15084] [pdf]
[]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
[]LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization (L. von Stumberg, P. Wenzel, N. Yang and D. Cremers), In International Conference on 3D Vision (3DV), 2020. ([arXiv][project page][video][supplementary][poster]) [bibtex]
[]4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving (P. Wenzel, R. Wang, N. Yang, Q. Cheng, Q. Khan, L. von Stumberg, N. Zeller and D. Cremers), In Proceedings of the German Conference on Pattern Recognition (GCPR), 2020. ([project page][arXiv][video]) [bibtex] [pdf]
[]Effective Version Space Reduction for Convolutional Neural Networks (J Liu, I Chiotellis, R Triebel and D Cremers), In European Conference on Machine Learning and Data Mining (ECML-PKDD), 2020. ([arxiv]) [bibtex] [pdf]
[]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]
2024 | 2023 | 2022 | 2021 | 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
[]Towards Generalizing Sensorimotor Control Across Weather Conditions (Q. Khan, P. Wenzel, D. Cremers and L. Leal-Taixé), In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. ([arXiv]) [bibtex] [pdf]
[]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
2024 | 2023 | 2022 | 2021 | 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]
[]Precursor microRNA Identification Using Deep Convolutional Neural Networks (B. T. Do, V. Golkov, G. E. Gürel and D. Cremers), In bioRxiv preprint 414656, 2018. (bioRxiv:414656) [bibtex] [pdf]
Conference and Workshop Papers
[]Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs (P. Wenzel, Q. Khan, D. Cremers and L. Leal-Taixé), In Conference on Robot Learning (CoRL), 2018. ([arXiv][videos][poster]) [bibtex] [pdf]
[]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],[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]
[]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]
2024 | 2023 | 2022 | 2021 | 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]
2024 | 2023 | 2022 | 2021 | 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
[]Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks (S. Sharifzadeh, I. Chiotellis, R. Triebel and D. Cremers), In , NIPS Workshops, 2016. ([arxiv]) [bibtex] [pdf]
[]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%)
2024 | 2023 | 2022 | 2021 | 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
Computer Vision Group

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

Follow us on:

News

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

31.08.2022

Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.

More