| Dropout sampling for robust object detection in open-set conditions D Miller, L Nicholson, F Dayoub, N Sünderhauf 2018 IEEE International Conference on Robotics and Automation (ICRA), 3243-3249, 2018 | 296 | 2018 |
| Probabilistic object detection: Definition and evaluation D Hall, F Dayoub, J Skinner, H Zhang, D Miller, P Corke, G Carneiro, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 163 | 2020 |
| Class anchor clustering: A loss for distance-based open set recognition D Miller, N Sunderhauf, M Milford, F Dayoub Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021 | 152 | 2021 |
| Evaluating merging strategies for sampling-based uncertainty techniques in object detection D Miller, F Dayoub, M Milford, N Sünderhauf 2019 international conference on robotics and automation (icra), 2348-2354, 2019 | 133 | 2019 |
| Uncertainty for identifying open-set errors in visual object detection D Miller, N Sünderhauf, M Milford, F Dayoub IEEE Robotics and Automation Letters 7 (1), 215-222, 2021 | 57 | 2021 |
| Density-aware nerf ensembles: Quantifying predictive uncertainty in neural radiance fields N Sünderhauf, J Abou-Chakra, D Miller 2023 IEEE International Conference on Robotics and Automation (ICRA), 9370-9376, 2023 | 53 | 2023 |
| Benchmarking Sampling-based Probabilistic Object Detectors. D Miller, N Sünderhauf, H Zhang, D Hall, F Dayoub CVPR Workshops 3, 6, 2019 | 33 | 2019 |
| SAFE: Sensitivity-aware features for out-of-distribution object detection S Wilson, T Fischer, F Dayoub, D Miller, N Sünderhauf Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 31 | 2023 |
| What’s in the black box? the false negative mechanisms inside object detectors D Miller, P Moghadam, M Cox, M Wildie, R Jurdak IEEE Robotics and Automation Letters 7 (3), 8510-8517, 2022 | 23 | 2022 |
| Why object detectors fail: Investigating the influence of the dataset D Miller, G Goode, C Bennie, P Moghadam, R Jurdak Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 13 | 2022 |
| Never mind the metrics-what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective D Lovell, D Miller, J Capra, AP Bradley International Conference on Machine Learning, 22702-22757, 2023 | 12* | 2023 |
| Addressing the Challenges of Open-World Object Detection D Pershouse, F Dayoub, D Miller, N Sünderhauf arXiv preprint arXiv:2303.14930, 2023 | 6 | 2023 |
| Uncertainty-aware lidar place recognition in novel environments K Mason, J Knights, M Ramezani, P Moghadam, D Miller 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | 5 | 2023 |
| Open-set recognition in the age of vision-language models D Miller, N Sünderhauf, A Kenna, K Mason European Conference on Computer Vision, 1-18, 2024 | 3 | 2024 |
| Epistemic uncertainty estimation for object detection in open-set conditions D Miller Queensland University of Technology, 2021 | 3 | 2021 |
| Probabilistic object detection with an ensemble of experts D Miller Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 3 | 2020 |
| Countering Backdoor Attacks in Image Recognition: A Survey and Evaluation of Mitigation Strategies K Dunnett, R Arablouei, D Miller, V Dedeoglu, R Jurdak arXiv preprint arXiv:2411.11200, 2024 | 1 | 2024 |
| Open-Vocabulary Part-Based Grasping T van Oort, D Miller, WN Browne, N Marticorena, J Haviland, ... arXiv preprint arXiv:2406.05951, 2024 | 1 | 2024 |
| Electric Vehicle Next Charge Location Prediction R Marlin, R Jurdak, A Abuadbba, S Ruj, D Miller IEEE Transactions on Intelligent Transportation Systems, 2024 | 1 | 2024 |
| Dropout variational inference improves object detection in open-set conditions D Miller, L Nicholson, F Dayoub, N Sünderhauf Bayesian Deep Learning Workshop at the Internation Conference on Neural …, 2017 | 1 | 2017 |
Niko SuenderhaufProfessor at Queensland University of Technology (QUT) Centre for RoboticsZweryfikowany adres z qut.edu.au