A benchmark and simulator for uav tracking M Müller, N Smith, B Ghanem European conference on computer vision, 445-461, 2016 | 1698 | 2016 |
DeepGCNs: Can GCNs Go as Deep as CNNs? G Li, M Müller, A Thabet, B Ghanem International Conference on Computer Vision (ICCV), 2019 | 1320 | 2019 |
End-to-end driving via conditional imitation learning F Codevilla, M Müller, A López, V Koltun, A Dosovitskiy 2018 IEEE International Conference on Robotics and Automation (ICRA), 1-9, 2018 | 1142 | 2018 |
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild M Müller, A Bibi, S Giancola, S Al-Subaihi, B Ghanem European conference on computer vision, 2018 | 850 | 2018 |
Context-aware correlation filter tracking M Müller, N Smith, B Ghanem Proc. of the IEEE Conference on Computer Vision and Pattern Recognition …, 2017 | 722 | 2017 |
Driving Policy Transfer via Modularity and Abstraction M Müller, A Dosovitskiy, B Ghanem, V Koltun Conference on Robotic Learning (CoRL), 2018 | 238 | 2018 |
Sim4CV: A photo-realistic simulator for computer vision applications M Müller, V Casser, J Lahoud, N Smith, B Ghanem International Journal of Computer Vision, 1-18, 2018 | 222* | 2018 |
Sgas: Sequential greedy architecture search G Li, G Qian, IC Delgadillo, M Muller, A Thabet, B Ghanem Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 218 | 2020 |
Training Graph Neural Networks with 1000 Layers G Li, M Müller, B Ghanem, V Koltun International Conference on Machine Learning (ICML), 2021 | 209 | 2021 |
Learning high-speed flight in the wild A Loquercio, E Kaufmann, R Ranftl, M Müller, V Koltun, D Scaramuzza Science Robotics 6 (59), eabg5810, 2021 | 202 | 2021 |
Target response adaptation for correlation filter tracking A Bibi, M Müller, B Ghanem European conference on computer vision, 419-433, 2016 | 188 | 2016 |
Deepgcns: Making gcns go as deep as cnns G Li, M Müller, G Qian, IC Delgadillo, A Abualshour, A Thabet, B Ghanem IEEE transactions on pattern analysis and machine intelligence 45 (6), 6923-6939, 2021 | 170 | 2021 |
Deep drone acrobatics E Kaufmann, A Loquercio, R Ranftl, M Müller, V Koltun, D Scaramuzza Robotics: Science and Systems (RSS), 2020 | 146 | 2020 |
Champion-level drone racing using deep reinforcement learning E Kaufmann, L Bauersfeld, A Loquercio, M Müller, V Koltun, ... Nature 620 (7976), 982-987, 2023 | 124 | 2023 |
Zoedepth: Zero-shot transfer by combining relative and metric depth SF Bhat, R Birkl, D Wofk, P Wonka, M Müller arXiv preprint arXiv:2302.12288, 2023 | 115 | 2023 |
Persistent aerial tracking system for UAVs M Müller, G Sharma, N Smith, B Ghanem Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International …, 2016 | 65 | 2016 |
OIL: Observational Imitation Learning G Li, M Müller, V Casser, N Smith, DL Michels, B Ghanem Robotics: Science and Systems (RSS), 2018 | 45* | 2018 |
Reaching the limit in autonomous racing: Optimal control versus reinforcement learning Y Song, A Romero, M Müller, V Koltun, D Scaramuzza Science Robotics 8 (82), eadg1462, 2023 | 42 | 2023 |
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation M Müller, V Casser, N Smith, DL Michels, B Ghanem European conference on computer vision workshops, UAVision2018, 2017 | 38* | 2017 |
SADA: semantic adversarial diagnostic attacks for autonomous applications A Hamdi, M Müller, B Ghanem Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 10901 …, 2020 | 29 | 2020 |