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 | 59 | 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 | 31 | 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 | 24 | 2019 |
Benchmarking Sampling-based Probabilistic Object Detectors. D Miller, N Sünderhauf, H Zhang, D Hall, F Dayoub CVPR Workshops 3, 2019 | 4 | 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 | 3* | 2021 |
Probabilistic Object Detection with an Ensemble of Experts D Miller European Conference on Computer Vision, 46-55, 2020 | | 2020 |
Probabilistic Object Detection: Definition and Evaluation-supplementary material D Hall, F Dayoub, J Skinner, H Zhang, D Miller, P Corke, G Carneiro, ... | | |
Dropout Variational Inference Improves Object Detection in Open-Set Conditions D Miller, L Nicholson, F Dayoub, N Sünderhauf | | |