Fetching the paper…
Reading the bibliography…
We present a deep learning framework for probabilistic pixel-wise semantic segmentation, which we term Bayesian SegNet.
Transforming neural-net output levels to probability distributions
J. Denker and Y. Lecun · 1991
Earlier work this paper cites.
A practical bayesian framework for backpropagation networks
D. J. MacKay · 1992
Earlier work this paper cites.
Active learning with statistical models
D. A. Cohn, Z. Ghahramani, and M. I. Jordan · 1996
Earlier work this paper cites.
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.
Sift flow: Dense correspondence across different scenes
C. Liu, J. Yuen, A. Torralba, J. Sivic, and W. T. Freeman · 2008
Earlier work this paper cites.
Semantic texton forests for image categorization and segmentation
J. Shotton, M. Johnson, and R. Cipolla · 2008
Earlier work this paper cites.
Semantic object classes in video: A high-definition ground truth database
G. J. Brostow, J. Fauqueur, and R. Cipolla · 2009
Earlier work this paper cites.
Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context
J. Shotton, J. Winn, C. Rother, and A. Criminisi · 2009
Earlier work this paper cites.
Combining appearance and structure from motion features for road scene understanding
P. Sturgess, K. Alahari, L. Ladicky, and P. H. Torr · 2009
Earlier work this paper cites.
Label propagation in video sequences
V. Badrinarayanan, F. Galasso, and R. Cipolla · 2010
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
What, where and how many? combining object detectors and crfs
L. Ladickỳ, P. Sturgess, K. Alahari, C. Russell, and P. H. Torr · 2010
Earlier work this paper cites.
Practical variational inference for neural networks
A. Graves · 2011
Earlier work this paper cites.
Structured class-labels in random forests for semantic image labelling
P. Kontschieder, S. Rota Buló, H. Bischof, and M. Pelillo · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Rgb-(d) scene labeling: Features and algorithms
X. Ren, L. Bo, and D. Fox · 2012
Cited alongside, same era.
Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
Cited alongside, same era.
Local label descriptor for example based semantic image labeling
Y. Yang, Z. Li, L. Zhang, C. Murphy, J. Ver Hoeve, and H. Jiang · 2012
Cited alongside, same era.
Indoor semantic segmentation using depth information
C. Couprie, C. Farabet, L. Najman, and Y. LeCun · 2013
Cited alongside, same era.
Real-time human pose recognition in parts from single depth images
J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore · 2013
Neural decision forests for semantic image labelling
S. Rota Bulo and P. Kontschieder · 2014
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Later among the works it cites.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Later among the works it cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Later among the works it cites.
Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling
V. Badrinarayanan, A. Handa, and R. Cipolla · 2015
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Superparsing
J. Tighe and S. Lazebnik · 2013
Cited alongside, same era.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2014
Cited alongside, same era.
D. Eigen and R. Fergus · 2014
Cited alongside, same era.
Learning rich features from rgb-d images for object detection and segmentation
S. Gupta, R. Girshick, P. Arbeláez, and J. Malik · 2014
Cited alongside, same era.
Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2015
Closest in time.
Bayesian convolutional neural networks with bernoulli approximate variational inference
Y. Gal and Z. Ghahramani · 2015
Closest in time.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2015
Closest in time.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Closest in time.
Modelling uncertainty in deep learning for camera relocalization
A. Kendall and R. Cipolla · 2015
Closest in time.
Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
Closest in time.
Sun rgb-d: A rgb-d scene understanding benchmark suite
S. Song, S. P. Lichtenberg, and J. Xiao · 2015
Closest in time.
Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr · 2015
Closest in time.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
Closest in time.