9 Publications

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[9]
2019 | Conference Paper | IST-REx-ID: 7468 | OA
P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol. 2019–June.
View | DOI | Download Preprint (ext.) | arXiv
 
[8]
2018 | Conference Paper | IST-REx-ID: 5978 | OA
S. Haller, P. Swoboda, and B. Savchynskyy, “Exact MAP-inference by confining combinatorial search with LP relaxation,” in Proceedings of the 32st AAAI Conference on Artificial Intelligence, New Orleans, LU, United States, 2018, pp. 6581–6588.
View | Download Preprint (ext.) | arXiv
 
[7]
2018 | Journal Article | IST-REx-ID: 703 | OA
A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.
View | DOI | Download Preprint (ext.) | arXiv
 
[6]
2017 | Conference Paper | IST-REx-ID: 641
V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334.
View | DOI
 
[5]
2017 | Conference Paper | IST-REx-ID: 646 | OA
J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.
View | DOI | Download Submitted Version (ext.)
 
[4]
2017 | Conference Paper | IST-REx-ID: 915 | OA
P. Swoboda and B. Andres, “A message passing algorithm for the minimum cost multicut problem,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4990–4999.
View | Files available | DOI
 
[3]
2017 | Conference Paper | IST-REx-ID: 916 | OA
P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, and B. Savchynskyy, “A study of lagrangean decompositions and dual ascent solvers for graph matching,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 7062–7071.
View | Files available | DOI
 
[2]
2017 | Conference Paper | IST-REx-ID: 917 | OA
P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean decomposition of combinatorial problems,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.
View | Files available | DOI
 
[1]
2016 | Research Data | IST-REx-ID: 5557 | OA
P. Swoboda, “Synthetic discrete tomography problems.” IST Austria, 2016.
View | Files available | DOI
 

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9 Publications

Mark all

[9]
2019 | Conference Paper | IST-REx-ID: 7468 | OA
P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol. 2019–June.
View | DOI | Download Preprint (ext.) | arXiv
 
[8]
2018 | Conference Paper | IST-REx-ID: 5978 | OA
S. Haller, P. Swoboda, and B. Savchynskyy, “Exact MAP-inference by confining combinatorial search with LP relaxation,” in Proceedings of the 32st AAAI Conference on Artificial Intelligence, New Orleans, LU, United States, 2018, pp. 6581–6588.
View | Download Preprint (ext.) | arXiv
 
[7]
2018 | Journal Article | IST-REx-ID: 703 | OA
A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.
View | DOI | Download Preprint (ext.) | arXiv
 
[6]
2017 | Conference Paper | IST-REx-ID: 641
V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334.
View | DOI
 
[5]
2017 | Conference Paper | IST-REx-ID: 646 | OA
J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.
View | DOI | Download Submitted Version (ext.)
 
[4]
2017 | Conference Paper | IST-REx-ID: 915 | OA
P. Swoboda and B. Andres, “A message passing algorithm for the minimum cost multicut problem,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4990–4999.
View | Files available | DOI
 
[3]
2017 | Conference Paper | IST-REx-ID: 916 | OA
P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, and B. Savchynskyy, “A study of lagrangean decompositions and dual ascent solvers for graph matching,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 7062–7071.
View | Files available | DOI
 
[2]
2017 | Conference Paper | IST-REx-ID: 917 | OA
P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean decomposition of combinatorial problems,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.
View | Files available | DOI
 
[1]
2016 | Research Data | IST-REx-ID: 5557 | OA
P. Swoboda, “Synthetic discrete tomography problems.” IST Austria, 2016.
View | Files available | DOI
 

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