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Annotating images with pixel-wise labels is a time-consuming and costly process.
Algorithms for the reduction of the number of points required to represent a digitized line or its caricature
David H Douglas and Thomas K Peucker · 1973
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Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules
Junji Shiraishi, Shigehiko Katsuragawa, Junpei Ikezoe, Tsuneo Matsumoto, Takeshi Kobayashi, Ken-ichi Komatsu, Mitate Matsui, Hiroshi Fujita, Yoshie Kodera, and Kunio Doi · 2000
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” grabcut” interactive foreground extraction using iterated graph cuts
Carsten Rother, Vladimir Kolmogorov, and Andrew Blake · 2004
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Active learning for probability estimation using jensen-shannon divergence
Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, and Raymond Mooney · 2005
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Nonparametric scene parsing: Label transfer via dense scene alignment
Ce Liu, Jenny Yuen, and Antonio Torralba · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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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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Superparsing: Scalable nonparametric image parsing with superpixels
J. Tighe and S. Lazebnik · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2013
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Beat the mturkers: Automatic image labeling from weak 3d supervision
Liang-Chieh Chen, Sanja Fidler, Alan Yuille, and Raquel Urtasun · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Imagenet auto-annotation with segmentation propagation
Matthieu Guillaumin, Daniel Küttel, and Vittorio Ferrari · 2014
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Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Stefan Jaeger, Sema Candemir, Sameer Antani, Yì-Xiáng J Wáng, Pu-Xuan Lu, and George Thoma · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Microsoft coco: Common objects in context, 2015
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár · 2015
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Fully convolutional networks for semantic segmentation, 2015
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun · 2015
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Imagenet large scale visual recognition challenge, 2015
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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The cityscapes dataset for semantic urban scene understanding, 2016
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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A point set generation network for 3d object reconstruction from a single image, 2016
Haoqiang Fan, Hao Su, and Leonidas Guibas · 2016
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Virtual worlds as proxy for multi-object tracking analysis
Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig · 2016
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What makes imagenet good for transfer learning?, 2016
Minyoung Huh, Pulkit Agrawal, and Alexei A. Efros · 2016
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Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun · 2016
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Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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CARLA: an open urban driving simulator
Alexey Dosovitskiy, Germán Ros, Felipe Codevilla, Antonio M. López, and Vladlen Koltun · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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A simple framework for contrastive learning of visual representations, 2020
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning, 2020
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark
MMSegmentation Contributors · 2020
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Meta-sim2: Unsupervised learning of scene structure for synthetic data generation
Jeevan Devaranjan, Amlan Kar, and Sanja Fidler · 2020
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Taming transformers for high-resolution image synthesis, 2020
Patrick Esser, Robin Rombach, and Björn Ommer · 2020
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Learning high-resolution domain-specific representations with a gan generator, 2020
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Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Efficient annotation of segmentation datasets with polygon-rnn++
David Acuna, Huan Ling, Amlan Kar, and Sanja Fidler · 2018
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The power of ensembles for active learning in image classification
William H. Beluch, Tim Genewein, Andreas Nurnberger, and Jan M. Kohler · 2018
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Demystifying MMD GANs
Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Multimodal unsupervised image-to-image translation
Xun Huang, Ming-Yu Liu, Serge J. Belongie, and Jan Kautz · 2018
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Cost-sensitive active learning for intracranial hemorrhage detection
Weicheng Kuo, Christian Häne, Esther Yuh, Pratik Mukherjee, and Jitendra Malik · 2018
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Danil Galeev, Konstantin Sofiiuk, Danila Rukhovich, Mikhail Romanov, Olga Barinova, and Anton Konushin · 2020
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Bootstrap your own latent: A new approach to self-supervised learning, 2020
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Momentum contrast for unsupervised visual representation learning, 2020
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Analyzing and improving the image quality of stylegan, 2020
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Box2seg: Attention weighted loss and discriminative feature learning for weakly supervised segmentation
Viveka Kulharia, Siddhartha Chandra, Amit Agrawal, Philip Torr, and Ambrish Tyagi · 2020
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Federated simulation for medical imaging
Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F Frangi, and Sanja Fidler · 2020
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Unsupervised learning of dense visual representations, 2020
Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, and Aaron Courville · 2020
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Emerging properties in self-supervised vision transformers, 2021
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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An empirical study of training self-supervised vision transformers, 2021
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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A broad study on the transferability of visual representations with contrastive learning, 2021
Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Richard Radke, and Rogerio Feris · 2021
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Alias-free generative adversarial networks, 2021
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
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Supervised contrastive learning, 2021
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2021
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Discobox: Weakly supervised instance segmentation and semantic correspondence from box supervision
Shiyi Lan, Zhiding Yu, Christopher Bongsoo Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry Davis, and Anima Anandkumar · 2021
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Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization, 2021
Daiqing Li, Junlin Yang, Karsten Kreis, Antonio Torralba, and Sanja Fidler · 2021
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The deep bootstrap framework: Good online learners are good offline generalizers, 2021
Preetum Nakkiran, Behnam Neyshabur, and Hanie Sedghi · 2021
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Extracting foreground masks towards object recognition
A. Rosenfeld and D. Weinshall · 2021
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Repurposing gans for one-shot semantic part segmentation, 2021
Nontawat Tritrong, Pitchaporn Rewatbowornwong, and Supasorn Suwajanakorn · 2021
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Dense contrastive learning for self-supervised visual pre-training, 2021
Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li · 2021
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Region similarity representation learning, 2021
Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, and Trevor Darrell · 2021
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Linear semantics in generative adversarial networks
Jianjin Xu and Changxi Zheng · 2021
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Datasetgan: Efficient labeled data factory with minimal human effort
Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, and Sanja Fidler · 2021
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