1. Oberdiek, Philipp and Rottmann, Matthias and Fink, Gernot A.: Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 328-329
  2. Schwarz, Katja and Liao, Yiyi and Niemeyer, Michael and Geiger, Andreas: GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. In: Part of Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020). Presentation available here.
  3. Schutera, Mark and Hussein, Mostafa and Abhau, Jochen and Mikut, Ralf and Reischl, Markus: Night-to-Day: Online Image-to-Image Translation for Object Detection Within Autonomous Driving by Night. In: IEEE Transactions on Intelligent Vehicles
  4. Schutera, Mark and Hafner, Frank M. and Abhau, Jochen and Hagenmeyer, Veit and Mikut, Ralf and Reischl, Markus: Cuepervision: self-supervised learning for continuous domain adaptation without catastrophic forgetting. In: Vision and Image Computing as part of the special issue: Advances in Domain Adaptation for Computer Vision.
  5. Monka, Sebastian and Halilaj, Lavdim and Rettinger, Achim: A  Survey on Visual Transfer Learning using Knowledge Graphs. In: Semantic Web Journal (SWJ, IOS Press).
  6. Monka, Sebastian and Halilaj, Lavdim and Schmid, Stefan and Rettinger, Achim: ConTraKG: Contrastive-based Transfer Learning for Visual Object Recognition using Knowledge Graphs. In: arXiv.
  7. Sauer, Axel and Geiger, Andreas: Counterfactual Generative Networks. In: ICLR 2021 (International Conference on Learning Representations).
  8. Guerrero-Viu, Julia and Izquierdo, Sergio Izquierdo and Schröppel, Philipp and Brox, Thomas: Semi-Supervised Disparity Estimation with Deep Feature Reconstruction. In: Conference on Computer Vision and Pattern Recognition 2021 (CVPR).
  9. Müller, Norman and Wong, Yu-Shiang and J. Mitra, Niloy and Dai, Angela and Niessner, Matthias: Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences. In: Conference on Computer Vision and Pattern Recognition 2021 (CVPR).
  10. Prakash, Adity and Chitta, Kashyap and Geiger, Andreas: Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. In: Conference on Computer Vision and Pattern Recognition 2021 (CVPR).
  11. Hanselmann, Niklas and Schneider, Nick and Ortelt, Benedikt and Geiger, Andreas: Learning Cascaded Detection Tasks with Weakly—supervised Domain Adaptation. In: IEEE Intelligent Vehicles Symposium.
  12. Triess, Larissa T. and Dreissig, Mariella and Rist, Christoph Bernd and Zöllner, J. Marius: A  Survey on Deep Domain Adaptation for LiDAR Perception. In: IEEE Intelligent Vehicles Symposium.
  13. Niemeijer, Joshua and Schäfer, Jörg P.: Combining Semantic Self-Supervision and Self-Training for Domain Adaptation in Semantic Segmentation. In: 2021 IEEE Intelligent Vehicles Symposium (IV).
  14. Hornauer, Julia and Nalpantidis, Lazaros and Belagiannis, Vasileios: Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware. In: ICCV2021, ERCVAD Workshop.
  15. Mantowsky, Sven and Heuer, Falk, and Bukhari, Saqib and Keckeisen, Michael and Schneider, Georg: ProAI: An Efficient Embedded AI Hardware for Automotive Applications – a Benchmark Study. In: ICCV2021, ERCVAD Workshop.
  16. Heuer, Falk and Mantowsky, Sven and Bukhari, Syed Saqib and Schneider, Georg: MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning Using an Anchor Free Approach. In: ICCV2021, ERCVAD  Workshop.
  17. Poucin, Florentin and Kraus, Andrea and Simon, Martin: Boosting Instance Segmentation With Synthetic Data: A Study To Overcome the Limits of Real World Data Sets. In: ICCV2021, ERCVAD  Workshop.
  18. Lyssenko, Maria and Gladisch, Christoph and Heinzemann, Christian and Woehrle, Matthias and Triebel, Rudolph: Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-Oriented Synthetic Data Generation in Crowded Scenes. In: ICCV2021, ERCVAD  Workshop.
  19. Chitta, Kashyap and Prakash, Aditya and Geiger, Andreas: NEAT: Neural Attention Fields for End-to-End Autonomous Driving. In: ICCV 2021.
  20. Triess, Larissa T. and Peter, David and Baur, Stefan A. and Zöllner, Marius J.: Quantifying point cloud realism through adversarially learned latent space representations. In: 2021 German Conference on Pattern Recognition (GCPR).
  21. Bouazizi, Arij and Wiederer, Julian and Kressel, Ulrich and Belagiannis, Vasileios: Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry. In: IEEE - International Conference on Automatic Face & Gesture Recognition

Hier werden die öffentlichen Präsentationen des Projektes gelistet.



Präsentation auf BMWi-/VDA-Veranstaltung

KI Delta Learning (pdf:3 MB)

KI Delta Learning Standardpräsentation

Standardpräsentation (pdf:3 MB)



Die wichtigsten Projektdaten im Überblick

KI_Delta_Learning_Facts_Figures_de.pdf(pdf:257 KB)




Pressemitteilungen des Projektes und unserer Partner:

Hochschule Reutlingen: (German)

Offis: (German) (English)

DLR: (German)

Universität Wuppertal: (German)

Universität Stuttgart: (German) (English)

InnoSent GmbH (German)

FZI - Forschungszentrum Informatik (German)

Daimler AG:

Porsche AG:

Volkswagen AG:




Medien Echo

Beiträge über KI Delta Learning:

Porsche Magazin 2/2021

Der kleine Unterschied (PDF)

Informationsdienst Wissenschaft