| 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 | 161 | 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 | 93 | 2020 |
| 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 | 75 | 2019 |
| 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 | 54* | 2021 |
| 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 | 18 | 2021 |
| Benchmarking Sampling-based Probabilistic Object Detectors. D Miller, N Sünderhauf, H Zhang, D Hall, F Dayoub CVPR Workshops 3, 6, 2019 | 17 | 2019 |
| 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 | 3 | 2022 |
| Epistemic uncertainty estimation for object detection in open-set conditions D Miller Queensland University of Technology, 2021 | 2 | 2021 |
| Uncertainty-Aware Lidar Place Recognition in Novel Environments K Mason, J Knights, M Ramezani, P Moghadam, D Miller arXiv preprint arXiv:2210.01361, 2022 | 1 | 2022 |
| Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields N Sünderhauf, J Abou-Chakra, D Miller arXiv preprint arXiv:2209.08718, 2022 | 1 | 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 | 1 | 2022 |
| Probabilistic object detection with an ensemble of experts D Miller Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 1 | 2020 |
| 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 |
| Never mind the metrics--what about the uncertainty? Visualising confusion matrix metric distributions D Lovell, D Miller, J Capra, A Bradley arXiv preprint arXiv:2206.02157, 2022 | | 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 | | |
| Probabilistic Object Detection: Definition and Evaluation-supplementary material D Hall, F Dayoub, J Skinner, H Zhang, D Miller, P Corke, G Carneiro, ... | | |
Niko Sünderhauf (Suenderhauf)Professor at Queensland University of Technology (QUT) Centre for RoboticsVerified email at qut.edu.au
