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Dimity Miller
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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
1612018
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
932020
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
752019
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
182021
Benchmarking Sampling-based Probabilistic Object Detectors.
D Miller, N Sünderhauf, H Zhang, D Hall, F Dayoub
CVPR Workshops 3, 6, 2019
172019
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
32022
Epistemic uncertainty estimation for object detection in open-set conditions
D Miller
Queensland University of Technology, 2021
22021
Uncertainty-Aware Lidar Place Recognition in Novel Environments
K Mason, J Knights, M Ramezani, P Moghadam, D Miller
arXiv preprint arXiv:2210.01361, 2022
12022
Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields
N Sünderhauf, J Abou-Chakra, D Miller
arXiv preprint arXiv:2209.08718, 2022
12022
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
12022
Probabilistic object detection with an ensemble of experts
D Miller
Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020
12020
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
12017
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, ...
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Articles 1–16