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We propose ViewAL, a novel active learning strategy for semantic segmentation that exploits viewpoint consistency in multi-view datasets.
Query by committee
H Sebastian Seung, Manfred Opper, and Haim Sompolinsky · 1992
Earlier work this paper cites.
Heterogeneous uncertainty sampling for supervised learning
David D Lewis and Jason Catlett · 1994
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A sequential algorithm for training text classifiers
David D Lewis and William A Gale · 1994
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Committee-based sampling for training probabilistic classifiers
Ido Dagan and Sean P Engelson · 1995
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Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel · 2001
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2001
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Sample selection for statistical parsing
Rebecca Hwa · 2004
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Active learning using pre-clustering
Hieu T Nguyen and Arnold Smeulders · 2004
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Active learning with multiple views
Ion Muslea, Steven Minton, and Craig A Knoblock · 2006
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Margin-based active learning for structured output spaces
Dan Roth and Kevin Small · 2006
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A comparison and evaluation of multi-view stereo reconstruction algorithms
Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski · 2006
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Incorporating diversity and density in active learning for relevance feedback
Zuobing Xu, Ram Akella, and Yi Zhang · 2007
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Labelme: a database and web-based tool for image annotation
Bryan C Russell, Antonio Torralba, Kevin P Murphy, and William T Freeman · 2008
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven · 2008
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Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray · 2008
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Multi-class active learning for image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
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Active learning literature survey
Burr Settles · 2009
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Multi-view multi-label active learning for image classification
Xiaoyu Zhang, Jian Cheng, Changsheng Xu, Hanqing Lu, and Songde Ma · 2009
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Seeds: Superpixels extracted via energy-driven sampling
Michael Van den Bergh, Xavier Boix, Gemma Roig, Benjamin de Capitani, and Luc Van Gool · 2012
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Active learning for semantic segmentation with expected change
Alexander Vezhnevets, Joachim M. Buhmann, and Vittorio Ferrari · 2012
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, and Yann LeCun · 2013
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Scalable object detection using deep neural networks
Dumitru Erhan, Christian Szegedy, Alexander Toshev, and Dragomir Anguelov · 2014
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Selecting influential examples: Active learning with expected model output changes
Alexander Freytag, Erik Rodner, and Joachim Denzler · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Pixelwise view selection for unstructured multi-view stereo
Johannes L Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 2016
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Cost-effective active learning for deep image classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin · 2016
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Convolutional pose machines
Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Matterport3d: Learning from rgb-d data in indoor environments
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang · 2017
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Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Fast r-cnn
Ross Girshick · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
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 Girshick, and Jian Sun · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Cost-effective active learning for melanoma segmentation
Marc Gorriz, Axel Carlier, Emmanuel Faure, and Xavier Giro-i Nieto · 2017
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Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation?
John McCormac, Ankur Handa, Stefan Leutenegger, and Andrew J.Davison · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener and Silvio Savarese · 2017
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Suggestive annotation: A deep active learning framework for biomedical image segmentation
Lin Yang, Yizhe Zhang, Jianxu Chen, Siyuan Zhang, and Danny Z Chen · 2017
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Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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The power of ensembles for active learning in image classification
William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Large-scale visual active learning with deep probabilistic ensembles
Kashyap Chitta, Jose M Alvarez, and Adam Lesnikowski · 2018
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Cereals-cost-effective region-based active learning for semantic segmentation
Radek Mackowiak, Philip Lenz, Omair Ghori, Ferran Diego, Oliver Lange, and Carsten Rother · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Learning loss for active learning
Donggeun Yoo and In So Kweon · 2019
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