• WS23: Practical course Expert-Level Deep Learning and Creation of Deep Learning Methods with golkov, Lu Sang and Linus Härenstam-Nielsen.
• SS23: Practical course Learning For Self-Driving Cars and Intelligent Systems with Mariia Gladkova.
• WS22: Practical course Learning For Self-Driving Cars and Intelligent Systems with Mariia Gladkova.
• SS22: Practical course Learning For Self-Driving Cars and Intelligent Systems with Mariia Gladkova.
• WS21/22: Practical course Learning For Self-Driving Cars and Intelligent Systems with Mariia Gladkova.
• SS21: Practical course Learning For Self-Driving Cars and Intelligent Systems
• WS20/21: Practical course Learning For Self-Driving Cars and Intelligent Systems
• SS20: Practical course Learning For Self-Driving Cars and Intelligent Systems with News.
• WS19/20: Practical course Learning For Self-Driving Cars and Intelligent Systems with Yuesong Shen and News.
• SS19: Practical course Hands-on Deep Learning for Computer Vision and Biomedicine with golkov and News.

In case you are interested in doing an IDP, Guided Research, Bachelor/Master thesis, in the areas of Multi-modal deep learning, Graphical neural networks, active learning, vehicle control feel free to send your application including resume and transcripts and a short description of the area of interest.

• Learning algorithms for Combinatorial Optimization Problems
• End-to-end autonomous driving with multi-sensor fusion using Transformers.
• Domain adaptation using conditional diffusion models

• Investigating Distributed Generative Models for Multi-Agent Navigation [Master Thesis, Javier Martínez Peña]
• Speech Driven Neural Head Avatars [Master Thesis, Vitalii Rusinov]
• Distributed Obstacle Avoidance of Multiple Vehicles using Reinforcement Learning [Bachelor Thesis, Felix Förster]
• Augmented Pseudo-LiDAR for Self-Supervised Vehicle Control [IDP, Jonathan Schmidt]
• Learning robust vehicle Control from multiple images. [IDP, Idil Sülö]
• Offline Reinforcement Learning for vehicle control. [IDP, Samuel Weber]
• VentriloquistNet: Leveraging Speech Cues to Generate Naturalistic Talking Head Motions. [Master Thesis, Deepan Das]
• 3D Spatial Motion Estimation of Image Pixels. [Guided Research, Tong Yan Chan]
• Active Learning For Reducing The Labelling Effort in Semantic Segmentation. [Application Project, Mohab Ghanem]
• Self-supervised Novel View Image Synthesis for Improving Performance of Driving Algorithms.[Master Thesis, Yiu Ting Tang]
• Relative Pose Estimation using Novel View Synthesis for Relocalization.[Guided Research, Melis Öcal]
• Deep Active learning on Graphical networks for semantic segmentation.[Master Thesis, Viktor Drobnyi]
• Model Pruning for faster inference [Guided Research, Paul Ursulean]
• Self-Supervised Vehicle Control on Sparse 3D point clouds [Bachelor Thesis, Florian Müller]
• IMU based pose estimation using deep neural networks [IDP, Nicholas Gao]

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Journal Articles
2024
[]		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]
2023
[]		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]
2020
[]		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
2024
[]		Enhancing the Performance of Multi-Vehicle Navigation in Unstructured Environments using Hard Sample Mining (Y Ma, A Li, Q Khan and D Cremers), In arXiv preprint arXiv:2409.05119, 2024.  [bibtex]
Conference and Workshop Papers
2024
[]		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]
[]		Improving the Detection of Air-Voids and Aggregates in Images of Concrete Using Generative AI (Q Khan, M Hassan, V Kostic, V Golkov, C Gehlen and D Cremers), In GNI Symposium on AI for the Built World, 2024.  [bibtex]
2023
[]		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]
2022
[]		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] [pdf]
[]		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]
2021
[]		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]
2020
[]		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]
2019
[]		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]
2018
[]		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]
[]		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]
2017
[]		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]