SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

pinheiro, 2014, Recurrent convolutional neural networks for scene labeling, Proc 31st Int Conf Mach Learn, 82

10.1109/CVPRW.2014.80

ren, 2012, RGB-(D) scene labeling: Features and algorithms, Proc IEEE Conf Comput Vis Pattern Recognit, 2759

10.1007/s11263-012-0574-z

10.1007/978-3-642-15561-1_51

10.1109/ICCV.2011.6126496

grangier, 2009, Deep convolutional networks for scene parsing, Proc ICML Deep Learn Workshop

farabet, 2012, Scene parsing with multiscale feature learning, purity trees, and optimal covers, Proc 29th Int Conf Mach Learn, 575

10.1109/CVPR.2013.79

10.1109/ICRA.2014.6907236

10.1007/s11263-007-0090-8

koltun, 2011, Efficient inference in fully connected CRFs with gaussian edge potentials, Proc Advances Neural Inf Process Syst

10.1109/CVPR.2016.350

rota, 2014, Neural decision forests for semantic image labelling, Proc IEEE Conf Comput Vis Pattern Recognit, 81

10.5244/C.23.62

10.1007/978-3-642-33786-4_27

brostow, 2008, Segmentation and recognition using structure from motion point clouds, Proc 10th Eur Conf Comput Vis, 44

10.1109/ICCV.2015.162

10.1109/ICCV.2015.304

10.1007/978-3-642-15561-1_31

handa, 2016, SceneNet: Understanding real world indoor scenes with synthetic data, Proc IEEE Conf Comput Vis Pattern Recognit

gal, 2015, Dropout as a Bayesian approximation: Insights and applications, Proc ICML Deep Learn Workshop

kendall, 2015, Bayesian SegNet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding, arXiv 1511 02680

10.1109/CVPR.2015.7298965

simonyan, 2014, Very deep convolutional networks for large-scale image recognition, arXiv 1409 1556

10.1007/s11263-014-0733-5

10.1109/CVPR.2015.7298655

10.1016/j.patrec.2008.04.005

silberman, 2012, Indoor segmentation and support inference from RGBD images, Proc 12th Eur Conf Comput Vis, 746

zitnick, 2014, Edge boxes: Locating object proposals from edges, Proc 13th Eur Conf Comput Vis, 391

10.1109/CVPR.2008.4587503

10.1109/CVPR.2012.6248074

ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv 1502 01032

badrinarayanan, 0, Understanding symmetries in deep networks

10.1109/ICCV.2009.5459211

10.1109/TPAMI.2004.1273918

long, 2016, Fully convolutional networks for semantic segmentation

10.5244/C.27.32

10.1145/2647868.2654889

10.1109/ICCV.2015.123

10.1109/ICCV.2009.5459469

10.1109/ICCV.2015.178

liu, 2015, ParseNet: Looking wider to see better, arXiv preprint arXiv 1506 01070

badrinarayanan, 2015, SegNet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, arXiv preprint arXiv 1505 03561

10.1007/s11263-015-0816-y

10.1109/ICCV.2015.304

papandreou, 2015, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv 1502 02734

yu, 2015, Multi-scale context aggregation by dilated convolutions, arXiv 1511 07122

ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, Proc Med Image Comput Comput -Assisted Intervention, 234

10.1007/978-3-7908-2604-3_16

hong, 2015, Decoupled deep neural network for semi-supervised semantic segmentation, Proc 28th Int Conf Neural Inf Process Syst, 1495

10.1109/CVPR.2007.383157

10.1109/CVPR.2014.119

10.1109/ICCV.2015.178

liang-chieh, 2015, Semantic image segmentation with deep convolutional nets and fully connected CRFs, Proc Int Conf Learn Representations

10.1109/TPAMI.2012.231

10.1109/CVPR.2015.7298594

socher, 2011, Parsing natural scenes and natural language with recursive neural networks, Proc 26th Int Conf Mach Learn, 129

10.1007/978-3-319-11206-0_9

eigen, 2014, Depth map prediction from a single image using a multi-scale deep network, Proc Advances Neural Inf Process Syst, 2366

10.1109/ICCV.2015.179

10.1109/CVPR.2010.5539957

10.1109/CVPR.2015.7298959

dong, 2014, Learning a deep convolutional network for image super-resolution, Proc 13th Eur Conf Comput Vis, 184

kavukcuoglu, 2010, Learning convolutional feature hierarchies for visual recognition, Proc Advances Neural Inf Process Syst, 1090

schwing, 2015, Fully connected deep structured networks, arXiv 1503 02351

lin, 2014, Microsoft COCO: Common objects in context, Proc 13th Eur Conf Comput Vis, 740

10.1109/CVPR.2015.7298642

lin, 2015, Efficient piecewise training of deep structured models for semantic segmentation, arXiv 1504 01013