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State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs).
Segmentation and recognition using structure from motion point clouds
G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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.
rmsprop adaptive learning
T. Tieleman and G. Hinton · 2012
Earlier work this paper cites.
Geometric context from video
S. H. Raza, M. Grundmann, and I. Essa · 2013
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Geometric context from video
S. H. Raza, M. Grundmann, and I. Essa · 2013
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Unrolling loopy top-down semantic feedback in convolutional deep networks
C. Gatta, A. Romero, and J. van de Weijer · 2014
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.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Earlier work this paper cites.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2015
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Earlier work this paper cites.
Lasagne: First release., Aug. 2015
S. Dieleman, J. Schlüter, C. Raffel, E. Olson, and et al · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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A. Kendall, V. Badrinarayanan, and R. Cipolla · 2015
Learning aligned cross-modal representations from weakly aligned data
L. Castrejon, Y. Aytar, C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
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L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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The importance of skip connections in biomedical image segmentation
M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal · 2016
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Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2016
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Feature space optimization for semantic video segmentation
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Deeply-supervised nets
C. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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You only look once: Unified, real-time object detection
J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi · 2015
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Faster R-CNN: towards real-time object detection with region proposal networks
S. Ren, K. He, R. B. Girshick, and J. Sun · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Deep end2end voxel2voxel prediction
D. Tran, L. D. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015
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A. Kundu, V. Vineet, and V. Koltun · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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Residual networks are exponential ensembles of relatively shallow networks
A. Veit, M. J. Wilber, and S. J. Belongie · 2016
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Reseg: A recurrent neural network-based model for semantic segmentation
F. Visin, M. Ciccone, A. Romero, K. Kastner, K. Cho, Y. Bengio, M. Matteucci, and A. Courville · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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Dataset loaders: a python library to load and preprocess datasets
A. R. F. Visin · 2017
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