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 | 62 | 2022 |
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 | 25 | 2020 |
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 | 15 | 2023 |
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, HPA Lensch IEEE Robotics and Automation Letters (RA-L), 2023 | 3 | 2023 |
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 | 3 | 2021 |
DualAD: Disentangling the Dynamic and Static World for End-to-End Driving S Doll, N Hanselmann, L Schneider, R Schulz, M Cordts, M Enzweiler, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2024 | | 2024 |
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 | | |