Embedded and Real-World Computer Vision in Autonomous Driving
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices With Runtime Optimization-
[pdf]
[bibtex]@InProceedings{Stacker_2021_ICCV, author = {St\"acker, Lukas and Fei, Juncong and Heidenreich, Philipp and Bonarens, Frank and Rambach, Jason and Stricker, Didier and Stiller, Christoph}, title = {Deployment of Deep Neural Networks for Object Detection on Edge AI Devices With Runtime Optimization}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {1015-1022} }
Description of Corner Cases in Automated Driving: Goals and Challenges-
[pdf]
[arXiv]
[bibtex]@InProceedings{Bogdoll_2021_ICCV, author = {Bogdoll, Daniel and Breitenstein, Jasmin and Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard and Fingscheidt, Tim and Z\"ollner, Marius}, title = {Description of Corner Cases in Automated Driving: Goals and Challenges}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {1023-1028} }
Semantic Concept Testing in Autonomous Driving by Extraction of Object-Level Annotations From CARLA-
[pdf]
[bibtex]@InProceedings{Gannamaneni_2021_ICCV, author = {Gannamaneni, Sujan and Houben, Sebastian and Akila, Maram}, title = {Semantic Concept Testing in Autonomous Driving by Extraction of Object-Level Annotations From CARLA}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {1006-1014} }
About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving-
[pdf]
[bibtex]@InProceedings{Reuse_2021_ICCV, author = {Reuse, Matthias and Simon, Martin and Sick, Bernhard}, title = {About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {979-987} }
Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-Oriented Synthetic Data Generation in Crowded Scenes-
[pdf]
[bibtex]@InProceedings{Lyssenko_2021_ICCV, author = {Lyssenko, Maria and Gladisch, Christoph and Heinzemann, Christian and Woehrle, Matthias and Triebel, Rudolph}, title = {Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-Oriented Synthetic Data Generation in Crowded Scenes}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {988-996} }
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware-
[pdf]
[arXiv]
[bibtex]@InProceedings{Hornauer_2021_ICCV, author = {Hornauer, Julia and Nalpantidis, Lazaros and Belagiannis, Vasileios}, title = {Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {954-962} }
perf4sight: A Toolflow To Model CNN Training Performance on Edge GPUs-
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[supp]
[arXiv]
[bibtex]@InProceedings{Rajagopal_2021_ICCV, author = {Rajagopal, Aditya and Bouganis, Christos-Savvas}, title = {perf4sight: A Toolflow To Model CNN Training Performance on Edge GPUs}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {963-971} }
Boosting Instance Segmentation With Synthetic Data: A Study To Overcome the Limits of Real World Data Sets-
[pdf]
[supp]
[bibtex]@InProceedings{Poucin_2021_ICCV, author = {Poucin, Florentin and Kraus, Andrea and Simon, Martin}, title = {Boosting Instance Segmentation With Synthetic Data: A Study To Overcome the Limits of Real World Data Sets}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {945-953} }
MEAL: Manifold Embedding-Based Active Learning-
[pdf]
[arXiv]
[bibtex]@InProceedings{Sreenivasaiah_2021_ICCV, author = {Sreenivasaiah, Deepthi and Otterbach, Johannes and Wollmann, Thomas}, title = {MEAL: Manifold Embedding-Based Active Learning}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {1029-1037} }
ProAI: An Efficient Embedded AI Hardware for Automotive Applications - A Benchmark Study-
[pdf]
[bibtex]@InProceedings{Mantowsky_2021_ICCV, author = {Mantowsky, Sven and Heuer, Falk and Bukhari, Saqib and Keckeisen, Michael and Schneider, Georg}, title = {ProAI: An Efficient Embedded AI Hardware for Automotive Applications - A Benchmark Study}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {972-978} }
MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning Using an Anchor Free Approach-
[pdf]
[arXiv]
[bibtex]@InProceedings{Heuer_2021_ICCV, author = {Heuer, Falk and Mantowsky, Sven and Bukhari, Saqib and Schneider, Georg}, title = {MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning Using an Anchor Free Approach}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {997-1005} }