Fetching the paper…
Reading the bibliography…
In this work, we for the first time present a method for detecting label errors in image datasets with semantic segmentation, i.e., pixel-wise class labels.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman · 2014
Earlier work this paper cites.
Detection of annotation errors in corpora
Markus Dickinson · 2015
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szeged, Dumitru Erhan, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper R. R. Uijlings, and Vittorio Ferrari · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
CARLA: an open urban driving simulator
Alexey Dosovitskiy, Germán Ros, Felipe Codevilla, Antonio M. López, and Vladlen Koltun · 2017
Earlier work this paper cites.
Carla: An open urban driving simulator
Alexey Dosovitskiy, Germán Ros, Felipe Codevilla, Antonio M. López, and Vladlen Koltun · 2017
Earlier work this paper cites.
Training deep neural-networks using a noise adaption layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
Earlier work this paper cites.
Efficient multi-scale 3d CNN with fully connected CRF for accurate brain lesion segmentation
Konstantinos Kamnitsas, Christian Ledig, Virginia F.J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, and Ben Glocker · 2017
Earlier work this paper cites.
Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Earlier work this paper cites.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama · 2018
Earlier work this paper cites.
Imperfect segmentation labels: How much do they matter?
Nicholas Heller, Joshua Dean, and Nikolaos Papanikolopoulos · 2018
Earlier work this paper cites.
Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, and Duncan Wilson · 2018
Earlier work this paper cites.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
Earlier work this paper cites.
A probabilistic u-net for segmentation of ambiguous images
Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, and Olaf Ronneberger · 2018
Cited alongside, same era.
Attention u-net: Learning where to look for the pancreas
Ozan Oktay, Jo Schlemper, Loïc Le Folgoc, M. J. Lee, Mattias P. Heinrich, Kazunari Misawa, Kensaku Mori, Steven G. McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, and Daniel Rueckert · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Improving semantic segmentation via video propagation and label relaxation
Yi Zhu, Karan Sapra, Fitsum A. Reda, Kevin J. Shih, Shawn D. Newsam, Andrew Tao, and Bryan Catanzaro · 2018
Cited alongside, same era.
Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Benben Liao, Guangyong Chen, and Shengyu Zhang · 2019
Novel information-theoretic loss function for training deep nets robust to label noise
Yilun Xu and Peng Cao · 2019
Later among the works it cites.
Semantic understanding of scenes through the ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
Later among the works it cites.
Controlled false negative reduction of minority classes in semantic segmentation
Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, and Hanno Gottschalk · 2020
Later among the works it cites.
An exploration of uncertainty information for segmentation quality assessment
Katharina Hoebel, Vincent Andrearczyk, Andrew Beers, Jay Patel, Ken Chang, Adrien Depeursinge, Henning Müller, and Jayashree Kalpathy-Cramer · 2020
Later among the works it cites.
Time-dynamic estimates of the reliability of deep semantic segmentation networks
Kira Maag, Matthias Rottmann, and Hanno Gottschalk · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Autonomous cars: Research results, issues, and future challenges
Rasheed Hussain and Sherali Zeadally · 2019
Cited alongside, same era.
Region-based active learning for efficient labeling in semantic segmentation
Tejaswi Kasarla, G Nagendar, Guruprasad M. Hegde, V. Balasubramanian, and C.V. Jawahar · 2019
Cited alongside, same era.
A survey of deep learning applications to autonomous vehicle control
Sampo Kuutti, Richard Bowden, Yaochu Jin, Phil Barber, and Saber Fallah · 2019
Cited alongside, same era.
An overview of deep learning in medical imaging focusing on mri
Alexander Selvikvåg Lundervold and Arvid Lundervold · 2019
Cited alongside, same era.
Confident learning: Estimating uncertainty in dataset labels
Curtis G. Northcutt, Lu Jiang, and Isaac L. Chuang · 2019
Cited alongside, same era.
Uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images
Matthias Rottmann and Marius Schubert · 2019
Cited alongside, same era.
Crowdproduktion von trainingsdaten – zur rolle von online-arbeit beim trainieren autonomer fahrzeuge, 2019
Florian A. Schmidt · 2019
Cited alongside, same era.
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
Later among the works it cites.
Hierarchical multi-scale attention for semantic segmentation
Andrew Tao, Karan Sapra, and Bryan Catanzaro · 2020
Later among the works it cites.
Learning to segment when experts disagree
Le Zhang, Ryutaro Tanno, Kevin Bronik, Chen Jin, Parashkev Nachev, Frederik Barkhof, Olga Ciccarelli, and Daniel C. Alexander · 2020
Later among the works it cites.
Disentangling human error from the ground truth in segmentation of medical images
Le Zhang, Ryutaro Tanno, Mou-Cheng Xu, Chen Jin, Joseph Jacob, Olga Ciccarelli, Frederik Barkhof, and Daniel C. Alexander · 2020
Later among the works it cites.
Segmentmeifyoucan: A benchmark for anomaly segmentation
Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Mathieu Salzmann, Pascal Fua, and Matthias Rottmann · 2021
Later among the works it cites.
Metabox+: A new region based active learning method for semantic segmentation using priority maps
Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk, and Matthias Rottmann · 2021
Later among the works it cites.
False positive detection and prediction quality estimation for lidar point cloud segmentation
Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, and Hanno Gottschalk · 2021
Later among the works it cites.
Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Di Feng, Christian Haase-Schutz, Lars Rosenbaum, Heinz Hertlein, Claudius Glaser, Fabian Timm, Werner Wiesbeck, and Klaus Dietmayer · 2021
Later among the works it cites.
Pervasive label errors in test sets destabilize machine learning benchmarks
Curtis G. Northcutt, Anish Athalye, and Jonas Mueller · 2021
Later among the works it cites.
Pytorch implementation of hierarchical multi-scale attention for semantic segmentation
Andrew Tao, Karan Sapra, and Bryan Catanzaro · 2021
Later among the works it cites.
Learning from pixel-level label noise: A new perspective for semi-supervised semantic segmentation
Rumeng Yi, Yaping Huang, Qingji Guan, Mengyang Pu, and Runsheng Zhang · 2022
Closest in time.