Fetching the paper…
Reading the bibliography…
In this work we propose a structured prediction technique that combines the virtues of Gaussian Conditional Random Fields (G-CRF) with Deep Learning: (a) our structured prediction task has a unique global optimum that is obtained exactly from the solution of a linear system (b) the gradients of our model parameters are analytically computed using closed form expressions, in contrast to the memory-demanding contemporary deep structured prediction approaches that rely on back-propagation-through-time, (c) our pairwise terms do not have to be simple hand-crafted expressions, as in the line of works building on the DenseCRF, but can rather be `discovered' from data through deep architectures, and (d) out system can trained in an end-to-end manner.
Numerical Recipes in C, 2nd Edition
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: · 1992
Earlier work this paper cites.
Matrix computations (3. ed.)
Golub, G.H., Loan, C.F.V.: · 1996
Earlier work this paper cites.
Matrix computations
Golub, G.H., Loan, V., F., C.: · 1996
Earlier work this paper cites.
Gaussian Markov Random Fields: Theory and Applications. Volume 104 of Monographs on Statistics and Applied Probability
Rue, H., Held, L.: · 2005
Earlier work this paper cites.
Random walks for image segmentation
Grady, L.: · 2006
Earlier work this paper cites.
Learning gaussian conditional random fields for low-level vision
Tappen, M.F., Liu, C., Adelson, E.H., Freeman, W.T.: · 2007
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Wainwright, M.J., Jordan, M.I.: · 2008
Earlier work this paper cites.
Efficient inference in fully connected crfs with gaussian edge potentials
Krähenbühl, P., Koltun, V.: · 2011
Earlier work this paper cites.
Scene parsing with multiscale feature learning, purity trees, and optimal covers
Farabet, C., Couprie, C., Najman, L., Lecun, Y.: · 2012
Earlier work this paper cites.
Multi-label energy minimization for object class segmentation
Couprie, C.: · 2012
Earlier work this paper cites.
Regression tree fields - an efficient, non-parametric approach to image labeling problems
Jancsary, J., Nowozin, S., Sharp, T., Rother, C.: · 2012
Earlier work this paper cites.
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 2013
Cited alongside, same era.
Semantic image segmentation with deep convolutional nets and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2014
Cited alongside, same era.
Microsoft coco: Common objects in context
2014
Cited alongside, same era.
Conditional random fields as recurrent neural networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.: · 2015
Cited alongside, same era.
Feedforward semantic segmentation with zoom-out features
Mostajabi, M., Yadollahpour, P., Shakhnarovich, G.: · 2015
Cited alongside, same era.
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: · 2015
Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation
Chen, L.C., Papandreou, G., Murphy, K., Yuille, A.L.: · 2015
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
Later among the works it cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
Eigen, D., Fergus, R.: · 2015
Later among the works it cites.
Gaussian conditional random field network for semantic segmentation
Vemulapalli, R., Tuzel, O., Liu, M.Y., Chellapa, R.: · 2016
Closest in time.
Deep gaussian conditional random field network: A model-based deep network for discriminative denoising
Vemulapalli, R., Tuzel, O., Liu, M.: · 2016
Closest in time.
Efficient piecewise training of deep structured models 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.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Cited alongside, same era.
Learning Deep Structured Models
Chen, L.C., Schwing, A.G., Yuille, A.L., Urtasun, R.: · 2015
Cited alongside, same era.
Matrix backpropagation for deep networks with structured layers
Ionescu, C., Vantzos, O., Sminchisescu, C.: · 2015
Cited alongside, same era.
Semantic image segmentation via deep parsing network
Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: · 2015
Cited alongside, same era.
Context-aware cnns for person head detection
Vu, T.H., Osokin, A., Laptev, I.: · 2015
Cited alongside, same era.
An introduction to the conjugate gradient method without the agonizing pain
Shewchuk, J.R.:
Cited in the paper.
Lin, G., Shen, C., Reid, I.D., van den Hengel, A.: · 2016
Closest in time.
Attention to scale: Scale-aware semantic image segmentation
Chen, L., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: · 2016
Closest in time.
Pushing the Boundaries of Boundary Detection using Deep Learning
Kokkinos, I.: · 2016
Closest in time.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Closest in time.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2016
Closest in time.
Ubernet: A ‘universal’ cnn for the joint treatment of low-, mid-, and high- level vision problems
Kokkinos, I.: · 2016
Closest in time.