Uncertainty Estimation in One-Stage Object Detection

B. Phan, R. Salay, K. Czarnecki, V. Abdelzad, T. Denouden, and S. Vernekar, “Calibrating uncertainties in object localization task,” arXiv:1811.11210 [cs.LG] , Nov 2018

Original

1905

Earlier work this paper cites.

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” International Journal of Robotics Research (IJRR) , 2013

2013

Earlier work this paper cites.

M. Abadi et al. , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015. [Online]. Available: www.tensorflow.org

2015

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , 2015

2015

Earlier work this paper cites.

Y. Gal and Z. Ghahramani, “Bayesian convolutional neural networks with Bernoulli approximate variational inference,” in 4th International Conference on Learning Representations (ICLR) workshop track , 2016

2016

Earlier work this paper cites.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “SSD: Single shot MultiBox detector,” in Computer Vision – ECCV 2016 . Springer International Publishing, 2016, pp. 21–37

2016

Earlier work this paper cites.

Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016

2016

Cited alongside, same era.

A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 5574–5584

2017

Cited alongside, same era.

J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy, “Speed/accuracy trade-offs for modern convolutional object detectors,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017

2017

Cited alongside, same era.

J. Redmon and A. Farhadi, “Yolo9000: Better, faster, stronger,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017

2017

Cited alongside, same era.

D. Miller, L. Nicholson, F. Dayoub, and N. Sünderhauf, “Dropout sampling for robust object detection in open-set conditions,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , May 2018, pp. 1–7

2018

Later among the works it cites.

J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv:1804.02767 [cs.CV] , Apr 2018. [Online]. Available: https://pjreddie.com/darknet/yolo/

Original

2018

Later among the works it cites.

M. T. Le, F. Diehl, T. Brunner, and A. Knol, “Uncertainty estimation for deep neural object detectors in safety-critical applications,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , Nov 2018, pp. 3873–3878

2018

Later among the works it cites.

S. Wirges, M. Reith- Braun, M. Lauer, and C. Stiller, “Capturing object detection uncertainty in multi-layer grid maps,” arXiv:1901.11284 [cs.RO] , Jan 2019

Original

2019

Closest in time.