The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs
Open accessAbstract
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 [1, 2] 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 [1, 3], but can rather be ‘discovered’ from data through deep architectures, and (d) out system can trained in an end-to-end manner. Building on standard tools from numerical analysis we develop very efficient algorithms for inference and learning, as well as a customized technique adapted to the semantic segmentation task. This efficiency allows us to explore more sophisticated architectures for structured prediction in deep learning: we introduce multi-resolution architectures to couple information across scales in a joint optimization framework, yielding systematic improvements. We demonstrate the utility of our approach on the challenging VOC PASCAL 2012 image segmentation benchmark, showing substantial improvements over strong baselines. We make all of our code and experiments available at https://github.com/siddharthachandra/gcrf.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213
Self-citationLearning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.348 · Cited by 862
Gaussian Conditional Random Field Network for Semantic Segmentation
Vemulapalli, R., Tuzel, O., Liu, M.-Y., & Chellappa, R. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.351 · Cited by 150
Research works printed but not linked: 26
- Mostajabi, M., Yadollahpour, P., Shakhnarovich, G.: Feedforward semantic segmentation with zoom-out features. In: CVPR (2015) · DOI · Paper page
- Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015) · DOI · Paper page
- Farabet, C., Couprie, C., Najman, L., Lecun, Y.: Scene parsing with multiscale feature learning, purity trees, and optimal covers. In: ICML (2012)
- Chen, L.C., Schwing, A.G., Yuille, A.L., Urtasun, R.: Learning deep structured models. In: ICML (2015)
- Vemulapalli, R., Tuzel, O., Liu, M.: Deep Gaussian conditional random field network: a model-based deep network for discriminative denoising. In: CVPR (2016) · DOI
- Ionescu, C., Vantzos, O., Sminchisescu, C.: Matrix backpropagation for deep networks with structured layers. In: ICCV (2015) · DOI · Paper page
- Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: NIPS (2011)
- Couprie, C.: Multi-label energy minimization for object class segmentation. In: 2012 Proceedings of the 20th European on Signal Processing Conference (EUSIPCO), pp. 2233–2237. IEEE (2012)
- Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: Semantic image segmentation via deep parsing network. In: CVPR, pp. 1377–1385 (2015) · DOI · Paper page
- Tappen, M.F., Liu, C., Adelson, E.H., Freeman, W.T.: Learning Gaussian conditional random fields for low-level vision. In: CVPR (2007) · DOI
- Jancsary, J., Nowozin, S., Sharp, T., Rother, C.: Regression tree fields - an efficient, non-parametric approach to image labeling problems. In: CVPR (2012) · DOI
- Vu, T.H., Osokin, A., Laptev, I.: Context-aware CNNs for person head detection. In: ICCV, pp. 2893–2901 (2015) · DOI
- Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd edn. Cambridge University Press, New York (1992)
- Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)
- Grady, L.: Random walks for image segmentation. PAMI 28, 1768–1783 (2006) · DOI · Paper page
- Golub, G.H., Loan, V., F., C: Matrix computations. 3(1–2), 510 (1996)
- Rue, H., Held, L.: Gaussian Markov Random Fields: Theory and Applications. Monographs on Statistics and Applied Probability, vol. 104. Chapman & Hall, London (2005) · DOI · Paper page
- Wainwright, M.J., Jordan, M.I.: Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn. 1(1–2), 136–138 (2008)
- Chen, L., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: CVPR (2016) · DOI · Paper page
- Kokkinos, I.: Pushing the boundaries of boundary detection using deep learning. In: ICLR (2016)
- Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: ECCV (2014) · DOI · Paper page
- Chen, L.C., Papandreou, G., Murphy, K., Yuille, A.L.: Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: ICCV (2015) · DOI · Paper page
- Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
- Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv:1606.00915 (2016) · arXiv
- Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: ICCV, pp. 2650–2658 (2015) · DOI · Paper page
- Kokkinos, I.: Ubernet: a universal cnn for the joint treatment of low-, mid-, and high- level vision problems. In: POCV Workshop (2016)
Cited by
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Self-citationChen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Self-citationEncoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
Lin, G., Milan, A., Shen, C., & Reid, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.549 · Cited by 3,287
“7 CSupelec-Res101 [4] 92.”
Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network
Peng, C., Zhang, X., Yu, G., Luo, G., & Sun, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.189 · Cited by 1,750
Metadata
- Authors
- Chandra, S., & Kokkinos, I.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 402-418
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2333621733
- Cited by
- 175
- References
- 7
- FWCI
- 9.52
- Type
- conference paper
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs. https://app.cochat.ai/papers/10.1007/978-3-319-46478-7_25
- The paper
- Chandra, S., & Kokkinos, I. (2016). Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-46478-7_25
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
