Gaussian Conditional Random Field Network for Semantic Segmentation
Abstract
In contrast to the existing approaches that use discrete Conditional Random Field (CRF) models, we propose to use a Gaussian CRF model for the task of semantic segmentation. We propose a novel deep network, which we refer to as Gaussian Mean Field (GMF) network, whose layers perform mean field inference over a Gaussian CRF. The proposed GMF network has the desired property that each of its layers produces an output that is closer to the maximum a posteriori solution of the Gaussian CRF compared to its input. By combining the proposed GMF network with deep Convolutional Neural Networks (CNNs), we propose a new end-to-end trainable Gaussian conditional random field network. The proposed Gaussian CRF network is composed of three sub-networks: (i) a CNN-based unary network for generating unary potentials, (ii) a CNN-based pairwise network for generating pairwise potentials, and (iii) a GMF network for performing Gaussian CRF inference. When trained end-to-end in a discriminative fashion, and evaluated on the challenging PASCALVOC 2012 segmentation dataset, the proposed Gaussian CRF network outperforms various recent semantic segmentation approaches that combine CNNs with discrete CRF models.
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In [52], a Gaussian Mean Field network was proposed and combined with CNNs where all parameters are trainable.
“In [52], a Gaussian Mean Field (GMF) network was proposed and combined with CNNs where all the parameters are trainable.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [52]
Mean-field approximation is often used for efficient inference of the CRF while allowing learning parameters by back-propagation.
“Alternatively, mean-field approximation [45], [50], [52] is often used for efficient inference of the CRF while allowing learning parameters by back-propagation.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [52]
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SAB Net: A Semantic Attention Boosting Framework for Semantic Segmentation
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Cross-Domain Scene Unsupervised Learning Segmentation With Dynamic Subdomains
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Efficient Attention Pyramid Network for Semantic Segmentation
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SCARF: A Semantic Constrained Attention Refinement Network for Semantic Segmentation
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High Resolution Feature Recovering for Accelerating Urban Scene Parsing
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SRRNet: A Semantic Representation Refinement Network for Image Segmentation
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An enhancement model based on dense atrous and inception convolution for image semantic segmentation
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Pooling Attention-based Encoder–Decoder Network for semantic segmentation
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- Authors
- Vemulapalli, R., Tuzel, O., Liu, M.-Y., & Chellappa, R.
- Affiliations
- University of Maryland, College Park (US)
- Mitsubishi Electric Research Laboratories (United States) (US)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 3224-3233
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2473131906
- Cited by
- 150
- References
- 61
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 8 · 2023: 10 · 2022: 13 · 2021: 25 · 2020: 26 · 2019: 25 · 2018: 20 · 2017: 15 · 2016: 1
- FWCI
- 7.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- semantic segmentation, mean-field inference, maximum a posteriori estimation, unary potentials, pair potential, end-to-end learning, convolutional neural networks, Pascal VOC 2012
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