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Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
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Expected information as expected utility
José M Bernardo · 1979
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A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
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Estimating the mean and variance of the target probability distribution
David A Nix and Andreas S Weigend · 1994
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds
Tilmann Gneiting, Larissa I Stanberry, Eric P Grimit, Leonhard Held, and Nicholas A Johnson · 2008
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Diagnosing error in object detectors
Derek Hoiem, Yodsawalai Chodpathumwan, and Qieyun Dai · 2012
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Contrasting probabilistic scoring rules
Reason L Machete · 2013
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Equivalence of distance-based and rkhs-based statistics in hypothesis testing
Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton, and Kenji Fukumizu · 2013
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Energy statistics: A class of statistics based on distances
Gábor J Székely and Maria L Rizzo · 2013
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Disco nets : Dissimilarity coefficients networks
Diane Bouchacourt, Pawan K Mudigonda, and Sebastian Nowozin · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Energy distance
Maria L Rizzo and Gábor J Székely · 2016
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The cramer distance as a solution to biased wasserstein gradients
Marc G Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, and Rémi Munos · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
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Focal loss for dense object detection
Uncertainty estimation in one-stage object detection
Florian Kraus and Klaus Dietmayer · 2019
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Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
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Lasernet: An efficient probabilistic 3d object detector for autonomous driving
Gregory P Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, and Carl K Wellington · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Jasper Snoek, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua Dillon, Jie Ren, and Zachary Nado · 2019
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Augpod: Augmentation-oriented probabilistic object detection
Chuan-Wei Wang, Chin-An Cheng, Ching-Ju Cheng, Hou-Ning Hu, Hung-Kuo Chu, and Min Sun · 2019
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Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Leveraging heteroscedastic aleatoric uncertainties for robust real-time lidar 3d object detection
Di Feng, Lars Rosenbaum, and Klaus Dietmayer · 2018
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Towards safe autonomous driving: Capture uncertainty in the deep neural network for lidar 3d vehicle detection
Di Feng, Lars Rosenbaum, and Klaus Dietmayer · 2018
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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
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Uncertainty estimation for deep neural object detectors in safety-critical applications
Michael Truong Le, Frederik Diehl, Thomas Brunner, and Alois Knol · 2018
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A mask-rcnn baseline for probabilistic object detection
Phil Ammirato and Alexander C Berg · 2019
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Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving
Jiwoong Choi, Dayoung Chun, Hyun Kim, and Hyuk-Jae Lee · 2019
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Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry Vetrov · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Can we trust you? on calibration of a probabilistic object detector for autonomous driving
Di Feng, Lars Rosenbaum, Claudius Glaeser, Fabian Timm, and Klaus Dietmayer · 2020
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A spectral energy distance for parallel speech synthesis
Alexey A Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, and Nal Kalchbrenner · 2020
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Probabilistic object detection: Definition and evaluation
David Hall, Feras Dayoub, John Skinner, Haoyang Zhang, Dimity Miller, Peter Corke, Gustavo Carneiro, Anelia Angelova, and Niko Sünderhauf · 2020
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Bayesod: A bayesian approach for uncertainty estimation in deep object detectors
Ali Harakeh, Michael Smart, and Steven L Waslander · 2020
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Deep mixture density network for probabilistic object detection
Yihui He and Jianren Wang · 2020
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The open images dataset v4
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al · 2020
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Localization uncertainty estimation for anchor-free object detection
Youngwan Lee, Joong-won Hwang, Hyung-Il Kim, Kimin Yun, and Joungyoul Park · 2020
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