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The vast majority of uncertainty quantification methods for deep object detectors such as variational inference are based on the network output.
Transforming neural-net output levels to probability distributions
John S Denker and Yann LeCun · 1990
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A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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The kitti vision benchmark suite
Andreas Geiger, P Lenz, Christoph Stiller, and Raquel Urtasun · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 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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XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Uncertainty in deep learning
Yarin Gal · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 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
Cited alongside, same era.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Cited alongside, same era.
Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
Cited alongside, same era.
Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
Cited alongside, same era.
Towards better confidence estimation for neural models
Vishal Thanvantri Vasudevan, Abhinav Sethy, and Alireza Roshan Ghias · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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The overlooked elephant of object detection: Open set
Akshay Dhamija, Manuel Gunther, Jonathan Ventura, and Terrance Boult · 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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Multivariate confidence calibration for object detection
Fabian Kuppers, Jan Kronenberger, Amirhossein Shantia, and Anselm Haselhoff · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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Dropout sampling for robust object detection in open-set conditions
Dimity Miller, Lachlan Nicholson, Feras Dayoub, and Niko Sünderhauf · 2018
Cited alongside, same era.
Relaxed softmax: Efficient confidence auto-calibration for safe pedestrian detection
Lukas Neumann, Andrew Zisserman, and Andrea Vedaldi · 2018
Cited alongside, same era.
Classification uncertainty of deep neural networks based on gradient information
Philipp Oberdiek, Matthias Rottmann, and Hanno Gottschalk · 2018
Cited alongside, same era.
Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
Cited alongside, same era.
Yolov3-in-pytorch
Haoyu Wu · 2018
Cited alongside, same era.
Metafusion: Controlled false-negative reduction of minority classes in semantic segmentation
Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, and Hanno Gottschalk · 2019
Cited alongside, same era.
Probabilistic object detection via deep ensembles
Zongyao Lyu, Nolan Gutierrez, Aditya Rajguru, and William J Beksi · 2020
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Time-dynamic estimates of the reliability of deep semantic segmentation networks
Kira Maag, Matthias Rottmann, and Hanno Gottschalk · 2020
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Improving video instance segmentation by light-weight temporal uncertainty estimates
Kira Maag, Matthias Rottmann, Fabian Hüger, Peter Schlicht, and Hanno Gottschalk · 2020
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Density estimation in representation space to predict model uncertainty
Tiago Ramalho and Miguel Miranda · 2020
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Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities
Matthias Rottmann, Pascal Colling, Thomas Paul Hack, Robin Chan, Fabian Hüger, Peter Schlicht, and Hanno Gottschalk · 2020
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Detection of false positive and false negative samples in semantic segmentation
Matthias Rottmann, Kira Maag, Robin Chan, Fabian Hüger, Peter Schlicht, and Hanno Gottschalk · 2020
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Metadetect: Uncertainty quantification and prediction quality estimates for object detection
Marius Schubert, Karsten Kahl, and Matthias Rottmann · 2020
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Resnest: Split-attention networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li, and Alexander Smola · 2020
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On the importance of gradients for detecting distributional shifts in the wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman · 2021
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Towards open world object detection
KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian · 2021
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Yodar: Uncertainty-based sensor fusion for vehicle detection with camera and radar sensors
Kamil Kowol., Matthias Rottmann., Stefan Bracke., and Hanno Gottschalk · 2021
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