Veröffentlichungen

publications

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
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.
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
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.
Monka, Sebastian and Halilaj, Lavdim and Rettinger, Achim: A Survey on Visual Transfer Learning using Knowledge Graphs. In: Semantic Web Journal (SWJ, IOS Press).
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.
Saikia, Tonmoy, Schmid, Cordelia, Brox, Thomas: Improving robustness to distribution shift by combining frequency biased models. CVPR 2021, 19.-25.06.2021.
Kalb, Tobias, Roschani, Masoud, Ruf, Miriam, Beyerer, Jürgen: Continual Learning for Class- and Domain-Incremental Semantic Segmentation. In: 32nd IEEE Intelligent Vehicles Symposium, 10.07.2021.
Helms, Domenik, Amende, Karl, Bukhari, Saqib, de Graaff, Thies, Frickenstein Alexander, Hafner, Frank, Hirscher, Tobias, Mantowsky, Sven, Schneider, Georg, Vemparala, Manoj-Rohit: Optimizing Neural Networks for Embedded Hardware. In: IEEE International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 19.07.2021.
Sauer, Axel and Geiger, Andreas: Counterfactual Generative Networks. In: ICLR 2021 (International Conference on Learning Representations).
Guerrero-Viu, Julia and Izquierdo, Sergio, Schröppel, Philipp and Brox, Thomas: Semi-Supervised Disparity Estimation with Deep Feature Reconstruction. In: Conference on Computer Vision and Pattern Recognition 2021 (CVPR).
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).
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).
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.
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.
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).
Hornauer, Julia and Nalpantidis, Lazaros and Belagiannis, Vasileios: Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware. In: ICCV2021, ERCVAD Workshop.
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.
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.
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.
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.
Chitta, Kashyap and Prakash, Aditya and Geiger, Andreas: NEAT: Neural Attention Fields for End-to-End Autonomous Driving. In: ICCV 2021.
Hubschneider, Christian, Birkenbach, Marius , Zöllner, J. Marius: Unsupervised Domain Adaptation via Shared Content Representation for Semantic Segmentation. In: 24th IEEE International Conference on Intelligent Transportation - ITSC2021, Indianapolis, 19.-22.09.2021.
Termöhlen, Jan-Aike, Klingner, Marvin, Brettin, Leon J., Schmidt, Nico M., Fingscheidt, Tim: Continual Unsupervised Domain Adaptation for Semantic Segmentation by Online Frequency Domain Style Transfer. In: 24th IEEE International Conference on Intelligent Transportation - ITSC2021, Indianapolis, 19.-22.09.2021.
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).
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.
Makansi, Osama, Çiçek, Özgün, Marrakchi, Yassine, Brox, Thomas: On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors. In: International Conference on Computer Vision (ICCV), 11.10.2021.
Wiederer, Julian, Bouazizi, Arij, Troina, Marco, Kressel, Ulrich, Belagiannis, Vasileios: Anomaly Detection in Multi-Agent Trajectories for Automated Driving. In: Conference on Robot Learning (CoRL), 08.11.2021.
Sauer, Axel, Chitta, Kashyap, Müller, Jens, Geiger, Andreas: Projected GANs Converge Faster. In: Neural Information Processing Systems, 06.12.2021.
Schmidt, Julian, Jordan, Julian, Gritschneder, Franz, Dietmayer, Klaus: CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention. In: IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, 23.-27.05.2022.
Chan, Robin, Lis, Krzysztof, Uhlemeyer, Svenja, Blum, Hermann, Honari, Sina, Siegwart, Roland, Fua, Pascal, Salzmann, Mathieu, Rottmann, Matthias: SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation. In: NeurIPS2021, 06.-14.12.2021.
Eskandar, George, Abdelsamad, Mohamed, Armanious, Karim, Yang, Bin: USIS: Unsupervised Semantic Image Synthesis.
Schwarz, Katja Schwarz, Liao, Yiyi, Geiger, Andreas: On the Frequency Bias of Generative Models. In: NeurIPS2021, 06.12.2021.
de Graaff, Thies, de Menezes, Arthur Ribeiro: Capsule Networks for Hierarchical Novelty Detection in Object Classification. In: IV 2022, 05.06.2022.
Uhlemeyer, Svenja, Rottmann, Matthias, Gottschalk, Hanno: Towards Unsupervised Open World Semantic Segmentation. In: CVPR 2022 New Orleans, 19.-24.06.2022.
Schrodi, Simon, Saikia, Tonmoy, Brox,Thomas: Towards Understanding Adversarial Robustness of Optical Flow Networks. In: CVPR 2022 New Orleans, 19.-24.06.2022.
Richter, Jasmine, Faion, Florian, Feng, Di, Becker, Paul Benedikt, Sielecki, Piotr, Glaeser, Claudius: Understanding the Domain Gap in LiDAR Object Detection Networks. In: 14. Uni-DAS e.V. Workshop Fahrerassistenz und automatisiertes Fahren, Berkheim, 09.-11.05.2022.
Eskandar, George, Abdelsamad, Mohamed, Armanious, Karim, Zhang, Shuai, Yang, Bin: Wavelet-based Unsupervised Label-to-Image Translation. In: ICASSP 2022, Singapore, 22.05.2022.
Zhou, Jingxing, Beyerer, Jürgen: Impacts of Data Anonymization on Semantic Segmentation. In: 33rd IEEE Intelligent Vehicles Symposium, 05.-09.06.2022.
Schmidt, Julian, Jordan, Julian, Raba, David, Welz, Tobias, Dietmayer, Klaus: MEAT: Maneuver Extraction from Agent Trajectories. In: 33rd IEEE Intelligent Vehicles Symposium, 05.-09.06.2022.
Hafner, Frank, Zeller, Matthias, Schutera, Mark, Abhau, Jochen , Kooij, Julian: BackboneAnalysis: Structured Insights into Compute Platforms from CNN Inference Latency. In: 2022 IEEE Intelligent Vehicles Symposium (IV), 04.-09.06.2022.
Marsden, Robert A., Bartler, Alexander, Döbler, Mario, Yang, Bin: Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation. In: WCCI 2022 (IJCNN 2022) Padua, 18.-23.07.2022.
Bouazizi, Arij, Kreßel, Ulrich, Holzbock, Adrian, Dietmayer, Klaus, Belagiannis, Vasileios: MotionMixer: MLP-based 3D Human Body Pose Forecasting. In: IJCAI-ECAI 22, 23.-29.07.2022.
Ding, Shuxiao, Rehder, Eike, Schneider, Lukas, Cordts, Marius, Gall, Jürgen: End-to-End Single Shot Detector using Graph-based Learnable Duplicate Removal. In: GCPR Konstanz, 27.-30.09.2022.
Schröppel, Philipp, Amiranashvili, Artemij, Bechtold, Jan, Brox, Thomas: A Benchmark and a Baseline for Robust Multi-view Depth Estimation. In: International Conference on 3D Vision 2022, Prague.
Sommer, Leonhard, Schröppel, Philipp, Brox, Thomas: SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection. In: GCPR Konstanz, 27.-30.09.2022.
Thirunavukkarasu, Arunachalam, Helms, Domenik Helms: Using Network Architecture Search for Optimizing Tensor Compression. In: ITEM Workshop, Grenoble, France, 19.-23.09.2022.
Osterwind, Adrian, Droste-Rehling, Julian, Vemparala, Manoj-Rohit, Helms, Domenik: Hardware Execution Time Prediction for Neural Network Layers. In: ITEM Workshop, Grenoble, France, 19.-23.09.2022.
Henning, Tabea, Grujic, Daniel, Werner, Tino, Kramer, Birte, Möhlmann, Eike: Verifying Safety of Safety-Critical Systems with Rare Events via Optimistic Optimization. In: 19^th International Conference on Quantitative Evaluation of SysTems (QEST 2022), Warsaw, 12.-16.09.2022.
Burghoff , Julian, Chan, Robin, Gottschalk, Hanno, Mütze, Annika, Riedlinger, Tobias, Rottmann, Matthias, Schubert, Marius: Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning. In: 11th NIC (The John von Neumann Institute for Computing) Symposium in Jülich.
George Eskandar, Robert A. Marsden, Pavithran Pandiyan, Mario Döbler, Karim Guirguis, Bin Yang: An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions, Kyoto, Japan
Julian Wiederer, Julian Schmidt, Ulrich Kreßel, Klaus Dietmayer, Vasileios Belagiannis: A Benchmark for Unsupervised Anomaly Detection in Multi-Agent Trajectories Macau, China
Julia Hornauer: Heatmap-based Out-of-Distribution Detection, WACV Waikoloa, Hawaii
Mariella Dreissig, Florian Piewak, Joschka Boedecker: On the calibration of underrepresented classes in LiDAR-based semantic segmentation, Kyoto, Japan
Joshua Niemeijer, Jörg P. Schäfer: Domain Adaptation and Generalization: A Low-Complexity Approach, Auckland, NZ
Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol, J. Marius Zöllner: Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Sarina Penquitt, Robin Chan, Hanno Gottschalk: Invertible Neural Networks Based on Matrix Factorization
Michael Essich, Markus Rehmann, Cristóbal Curio: Auxiliary Task-Guided CycleGAN for Black-Box Model Domain Adaptation, Waikoloa, Hawaii
Artem Savkin, Yida Wang, Sebastian Wirkert, Nassir Navab, Federico Tombari: Lidar Upsampling With Sliced Wasserstein Distance
Tobias Kalb, Niket Ahuja, Jingxing Zhou, Jürgen Beyerer: Effects of Architectures on Continual Semantic Segmentation

Hier werden die öffentlichen Präsentationen des Projektes gelistet.

Präsentationen

presentations

November 9, 2020

Präsentation auf BMWi-/VDA-Veranstaltung

KI Delta Learning (pdf:3 MB)

KI Delta Learning Standardpräsentation

Standardpräsentation (pdf:3 MB)

Präsentation auf der TÜV Süd Tagung Automatisiertes Fahren, 29./30. März 2022

TÜV Süd Tagung(pdf:2 MB)

Hier finden Sie die Ergebnisse des Projektes zum Download: Booklet, Präsentationen, Poster

Am 9. März 2023 fand das KI Delta Learning Final Event in Stuttgart-Vaihingen statt. Die Projektergebnisse wurden der Fachöffentlichkeit vorgestellt. Das Projekt präsentierte den rund 150 Besucherinnen und Besuchern in Präsentationen, Podiumsdiskussionen und 50 Postern die Ergebnisse aller KI Delta Learning Forschungsbereiche.