Fetching the paper…
Reading the bibliography…
The data-driven nature of deep learning (DL) models for semantic segmentation requires a large number of pixel-level annotations.
R. Caruana, Multitask learning, Machine Learning 28 (1) (1997) 41–75
1997
Earlier work this paper cites.
V. Vapnik, Statistical learning theory, Wiley, 1998
1998
Earlier work this paper cites.
J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K.-i. Komatsu, M. Matsui, H. Fujita, Y. Kodera, K. Doi, 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, American Journal of Roentgenology 174 (1) (2000) 71–74
2000
Earlier work this paper cites.
O. Chapelle, J. Weston, L. Bottou, V. Vapnik, Vicinal risk minimization, in: NIPS, 2001, pp. 416–422
2001
Earlier work this paper cites.
X. Zhu, Z. Ghahramani, Learning from labeled and unlabeled data with label propagation, Tech. Rep. CMU-CALD-02-107, Carnegie Mellon University (2002)
2002
Earlier work this paper cites.
F. Wang, C. Zhang, Label propagation through linear neighborhoods, IEEE Transactions on Knowledge and Data Engineering 20 (1) (2007) 55–67
2007
Earlier work this paper cites.
B. Triggs, J. J. Verbeek, Scene segmentation with crfs learned from partially labeled images, in: NIPS, 2008, pp. 1553–1560
2008
Earlier work this paper cites.
J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, N. D. Lawrence, Dataset shift in machine learning, The MIT Press, 2009
2009
Earlier work this paper cites.
X. Zhuang, K. S. Rhode, R. S. Razavi, D. J. Hawkes, S. Ourselin, A registration-based propagation framework for automatic whole heart segmentation of cardiac mri, IEEE TMI 29 (9) (2010) 1612–1625
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, A. Zisserman, The pascal visual object classes (voc) challenge, IJCV 88 (2) (2010) 303–338
2010
Earlier work this paper cites.
2011
Earlier work this paper cites.
N. Natarajan, I. S. Dhillon, P. K. Ravikumar, A. Tewari, Learning with noisy labels, in: NIPS, 2013, pp. 1196–1204
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, C. L. Zitnick, Microsoft coco: Common objects in context, in: European Conference on Computer Vision, Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: NIPS, 2014, pp. 2672–2680
2014
Earlier work this paper cites.
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: CVPR, 2015, pp. 3431–3440
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: MICCAI, 2015, pp. 234–241
2015
Earlier work this paper cites.
X. Zhuang, W. Bai, J. Song, S. Zhan, X. Qian, W. Shi, Y. Lian, D. Rueckert, Multiatlas whole heart segmentation of ct data using conditional entropy for atlas ranking and selection, Medical physics 42 (7) (2015) 3822–3833
2015
Cited alongside, same era.
D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, in: ICLR, 2015
2015
Cited alongside, same era.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, B. Schiele, The cityscapes dataset for semantic urban scene understanding, in: CVPR, 2016, pp. 3213–3223
2016
Cited alongside, same era.
P. Luc, C. Couprie, S. Chintala, J. Verbeek, Semantic segmentation using adversarial networks, in: NIPS Workshop on Adversarial Training, 2016
2016
Cited alongside, same era.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, Improved techniques for training gans, in: NIPS, 2016, pp. 2234–2242
Z. Han, B. Wei, A. Mercado, S. Leung, S. Li, Spine-gan: Semantic segmentation of multiple spinal structures, Medical Image Analysis 50 (2018) 23–35
2018
Later among the works it cites.
N. Dong, M. Kampffmeyer, X. Liang, Z. Wang, W. Dai, E. Xing, Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio, in: MICCAI, 2018, pp. 544–552
2018
Later among the works it cites.
Y. Zhou, Z. Li, S. Bai, C. Wang, X. Chen, M. Han, E. Fishman, A. L. Yuille, Prior-aware neural network for partially-supervised multi-organ segmentation, in: ICCV, 2019, pp. 10672–10681
2019
Later among the works it cites.
K. Dmitriev, A. E. Kaufman, Learning multi-class segmentations from single-class datasets, in: CVPR, 2019, pp. 9501–9511
2019
Later among the works it cites.
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, Y. Yoo, Cutmix: Regularization strategy to train strong classifiers with localizable features, in: ICCV, 2019, pp. 6023–6032
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in: CVPR, 2016, pp. 2818–2826
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: CVPR, 2016, pp. 770–778
2016
Cited alongside, same era.
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE TPAMI 40 (4) (2017) 834–848
2017
Cited alongside, same era.
T. N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, in: ICLR, 2017
2017
Cited alongside, same era.
P. Moeskops, M. Veta, M. W. Lafarge, K. A. Eppenhof, J. P. Pluim, Adversarial training and dilated convolutions for brain mri segmentation, in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, 2017, pp. 56–64
2017
Cited alongside, same era.
S. Motiian, Q. Jones, S. Iranmanesh, G. Doretto, Few-shot adversarial domain adaptation, in: NIPS, 2017, pp. 6670–6680
2017
Cited alongside, same era.
G. González, G. R. Washko, R. S. J. Estépar, Multi-structure segmentation from partially labeled datasets. application to body composition measurements on ct scans, in: Image Analysis for Moving Organ, Breast, and Thoracic Images, Springer, 2018, pp. 215–224
2018
Cited alongside, same era.
2019
Later among the works it cites.
A. Iscen, G. Tolias, Y. Avrithis, O. Chum, Label propagation for deep semi-supervised learning, in: CVPR, 2019, pp. 5070–5079
2019
Later among the works it cites.
B. Jiang, Z. Zhang, D. Lin, J. Tang, B. Luo, Semi-supervised learning with graph learning-convolutional networks, in: CVPR, 2019, pp. 11313–11320
2019
Later among the works it cites.
Z. Zhang, Z. Cui, C. Xu, Y. Yan, N. Sebe, J. Yang, Pattern-affinitive propagation across depth, surface normal and semantic segmentation, in: CVPR, 2019, pp. 4106–4115
2019
Later among the works it cites.
N. Dong, M. Xu, X. Liang, Y. Jiang, W. Dai, E. Xing, Neural architecture search for adversarial medical image segmentation, in: MICCAI, 2019, pp. 828–836
2019
Later among the works it cites.
R. Müller, S. Kornblith, G. E. Hinton, When does label smoothing help?, in: NIPS, 2019, pp. 4696–4705
2019
Later among the works it cites.
Z. Wang, N. Dong, S. D. Rosario, M. Xu, P. Xie, E. P. Xing, Ellipse detection of optic disc-and-cup boundary in fundus images, in: ISBI, IEEE, 2019, pp. 601–604
2019
Later among the works it cites.
X. Fang, P. Yan, Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction, IEEE TMI (2020)
2020
Closest in time.
Y. Ouali, C. Hudelot, M. Tami, Semi-supervised semantic segmentation with cross-consistency training, in: CVPR, 2020, pp. 12674–12684
2020
Closest in time.
S. Vandenhende, S. Georgoulis, B. De Brabandere, L. Van Gool, Branched multi-task networks: deciding what layers to share, in: BMVC, 2020
2020
Closest in time.
G. Shi, L. Xiao, Y. Chen, S. K. Zhou, Marginal loss and exclusion loss for partially supervised multi-organ segmentation, Medical Image Analysis (2021) 101979
2021
Closest in time.
S. Vandenhende, S. Georgoulis, W. Van Gansbeke, M. Proesmans, D. Dai, L. Van Gool, Multi-task learning for dense prediction tasks: A survey, IEEE TPAMI (2021)
2021
Closest in time.