Probabilistic Object Detection : Definition and Evaluation [Summary]
A summary of the paper Probabilistic Object Detection: Definition and Evaluation.
We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the lack of methods capable of assessing such probabilistic object detections, we present the new Probability-based Detection Quality measure (PDQ). PDQ evaluates both spatial and semantic uncertainty estimates and enables fine-grained analysis. Our aim is to encourage the development of new object detection approaches that provide detections with accurately estimated spatial and label uncertainties and are of critical importance for deployment on robots and embodied AI systems in the real world.
Paper Reference:
Hall, D., Dayoub F., Skinner, J., Zhang, H., Miller, D., Corke, P., Carneiro, G., Angelova, A., & Sünderhauf, N. (2020, March). Probabilistic object detection: Definition and evaluation. In 2020 IEEE Winter Conference on Applications of Computer Vision. IEEE.
Arxiv Link:
https://arxiv.org/abs/1811.10800
Presentation References:
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017, August). On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 1321-1330). JMLR. org.
Miller, D., Dayoub, F., Milford, M., & Sünderhauf, N. (2019, May). Evaluating merging strategies for sampling-based uncertainty techniques in object detection. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 2348-2354). IEEE.
Relevant other references:
Oksuz, K., Can Cam, B., Akbas, E., & Kalkan, S. (2018). Localization recall precision (LRP): A new performance metric for object detection. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 504-519).
Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. & Zitnick, C. L. (2014, September). Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.
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