Fetching the paper…
Reading the bibliography…
Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems.
A real-time algorithm for signal analysis with the help of the wavelet transform
M. Holschneider, R. Kronland-Martinet, J. Morlet, and P. Tchamitchian · 1990
Earlier work this paper cites.
Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 2011
Earlier work this paper cites.
Efficient inference in fully connected crfs with gaussian edge potentials
P. Krähenbühl and V. Koltun · 2011
Earlier work this paper cites.
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
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
A new performance measure and evaluation benchmark for road detection algorithms
J. Fritsch, T. Kuehnl, and A. Geiger · 2013
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
Higher order potentials in end-to-end trainable conditional random fields
A. Arnab, S. Jayasumana, S. Zheng, and P. Torr · 2015
Earlier work this paper cites.
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang · 2015
Earlier work this paper cites.
Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. Tobias Springenberg, and T. Brox · 2015
Cited alongside, same era.
Flownet: Learning optical flow with convolutional networks
P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Pushing the boundaries of boundary detection using deep learning
I. Kokkinos · 2015
Cited alongside, same era.
Efficient piecewise training of deep structured models for semantic segmentation
G. Lin, C. Shen, I. Reid, et al · 2015
Fast, exact and multi-scale inference for semantic image segmentation with deep gaussian crfs
S. Chandra and I. Kokkinos · 2016
Later among the works it cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
Later among the works it cites.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Later among the works it cites.
R-fcn: Object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
Later among the works it cites.
Laplacian reconstruction and refinement for semantic segmentation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Semantic image segmentation via deep parsing network
Z. Liu, X. Li, P. Luo, C.-C. Loy, and X. Tang · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2015
Cited alongside, same era.
Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr · 2015
Cited alongside, same era.
G. Ghiasi and C. Fowlkes · 2016
Later among the works it cites.
Optical flow with semantic segmentation and localized layers
L. Sevilla-Lara, D. Sun, V. Jampani, and M. J. Black · 2016
Later among the works it cites.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
Later among the works it cites.
Wavenet: A generative model for raw audio
A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu · 2016
Later among the works it cites.
High-performance semantic segmentation using very deep fully convolutional networks
Z. Wu, C. Shen, and A. v. d. Hengel · 2016
Later among the works it cites.
Semantic understanding of scenes through the ade20k dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2016
Later among the works it cites.