TR2016-078
Gaussian Conditional Random Field Network for Semantic Segmentation
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- "Gaussian Conditional Random Field Network for Semantic Segmentation", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016, pp. 3224-3233.BibTeX TR2016-078 PDF
- @inproceedings{Vemulapalli2016jun,
- author = {Vemulapalli, Raviteja and Tuzel, C. Oncel and Liu, Ming-Yu and Chellappa, Rama},
- title = {Gaussian Conditional Random Field Network for Semantic Segmentation},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = 2016,
- pages = {3224--3233},
- month = jun,
- url = {https://www.merl.com/publications/TR2016-078}
- }
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- "Gaussian Conditional Random Field Network for Semantic Segmentation", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016, pp. 3224-3233.
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Research Areas:
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.
Related News & Events
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NEWS MERL presents three papers at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Date: June 27, 2016 - June 30, 2016
Where: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV
MERL Contacts: Michael J. Jones; Tim K. Marks
Research Area: Machine LearningBrief- MERL researchers in the Computer Vision group presented three papers at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), which had a paper acceptance rate of 29.9%.