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Niklas Hanselmann
Niklas Hanselmann
Mercedes-Benz AG R&D & University of Tuebingen
Bestätigte E-Mail-Adresse bei mercedes-benz.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
N Hanselmann, K Renz, K Chitta, A Bhattacharyya, A Geiger
European Conference on Computer Vision (ECCV), 335–352, 2022
322022
Visibility guided nms: Efficient boosting of amodal object detection in crowded traffic scenes
N Gählert, N Hanselmann, U Franke, J Denzler
NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving, 2020
232020
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View
P Li, S Ding, X Chen, N Hanselmann, M Cordts, J Gall
International Joint Conference on Artificial Intelligence (IJCAI), 1080-1088, 2023
32023
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation
N Hanselmann, N Schneider, B Ortelt, A Geiger
IEEE Intelligent Vehicles Symposium (IV), 532-539, 2021
12021
STAR-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations
S Doll, N Hanselmann, L Schneider, R Schulz, M Enzweiler, H Lensch
arXiv preprint arXiv:2306.17602, 2023
2023
Unsupervised Domain Adaptive Object Detection with Class Label Shift Weighted Local Features
A Tan, N Hanselmann, S Ding, F Tombari, M Cordts
ECCV 2022 Workshop on Learning from Limited and Imperfect Data (L2ID), 118-133, 2022
2022
Supplementary Material for KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
N Hanselmann, K Renz, K Chitta, A Bhattacharyya, A Geiger
Supplementary For: Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation
N Hanselmann, N Schneider, B Ortelt, A Geiger
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