Downloads

Hier finden Sie Veröffentlichungen des Projekts zum Download.

Deliverables

Deliverables

Veröffentlichungen

publications

  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.

Präsentationen

presentations

Präsentation auf BMWi-/VDA-Veranstaltung

KI Delta Learning (pdf:3 MB)

KI Delta Learning Standardpräsentation

Standardpräsentation (pdf:3 MB)

Projektmaterialien

project-material

Die wichtigsten Projektdaten im Überblick

KI_Delta_Learning_Facts_Figures_de.pdf(pdf:257 KB)

Presse-Kit

press

Pressemitteilungen

Pressemitteilungen des Projektes und unserer Partner:

Hochschule Reutlingen:

https://www.hhz.de/de/aktuelles/news/news/2021-03-03-ki-delta-learning/ (German)

Offis:

https://www.offis.de/offis/projekt/ki-delta-learning.html (German)

https://www.offis.de/en/offis/project/ai-delta-learning.html (English)

DLR:

https://verkehrsforschung.dlr.de/de/projekte/ki-deltalearning (German)

Universität Wuppertal:

https://www.presse.uni-wuppertal.de/de/medieninformationen/2020/02/12/31499-rund-11-millionen-fuer-ki-forschung-an-der-bergischen-universitaet/ (German)

Universität Stuttgart:

https://www.uni-stuttgart.de/universitaet/aktuelles/presseinfo/Fahrstunden-fuer-die-Kuenstliche-Intelligenz/ (German)

https://www.uni-stuttgart.de/en/university/news/press-release/Driving-lessons-for-Artificial-Intelligence/ (English)

InnoSent GmbH

https://www.innosent.de/unternehmen/presse/view/article/ki-macht-autonomes-fahren-bereit-fuer-unterschiedliche-umgebungsszenarien/ (German)

FZI - Forschungszentrum Informatik

https://www.fzi.de/aktuelles/news/detail/artikel/die-ki-familie-bekommt-zuwachs-ki-wissen-nimmt-seine-forschungstaetigkeit-auf/ (German)

https://www.fzi.de/de/aktuelles/news/detail/artikel/halbzeit-bei-ki-delta-learning-erste-forschungsergebnisse-fuer-ein-skalierbares-automatisiertes-fahr/

Daimler AG:

https://media.daimler.com/marsMediaSite/de/instance/ko/Halbzeit-bei-KI-Delta-Learning-erste-Forschungsergebnisse-fuer-ein-skalierbares-automatisiertes-Fahren-werden-praesentiert.xhtml?oid=51620114

Porsche AG:

https://newsroom.porsche.com/de/2021/innovation/porsche-engineering-ki-delta-learning-forschungsprojekt-25984.html

Volkswagen AG:

https://www.volkswagenag.com/de/news/stories/2021/10/ai-delta-learning--simplifying-the-small-differences.html#

 

Pressemitteilungen

press

Medien Echo

Beiträge über KI Delta Learning:

Porsche Magazin 2/2021

Der kleine Unterschied (PDF)

Informationsdienst Wissenschaft

https://nachrichten.idw-online.de/2020/11/20/fahrstunden-fuer-die-kuenstliche-intelligenz/

 

Pressematerial

press

test