Alexander Kolesnikov
Lampert Group
8 Publications
2018 | Journal Article | IST-REx-ID: 563
Estimating barriers to gene flow from distorted isolation-by-distance patterns
H. Ringbauer, A. Kolesnikov, D. Field, N.H. Barton, Genetics 208 (2018) 1231–1245.
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H. Ringbauer, A. Kolesnikov, D. Field, N.H. Barton, Genetics 208 (2018) 1231–1245.
2018 | Thesis | IST-REx-ID: 197 
Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, IST Austria, 2018.
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A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, IST Austria, 2018.
2017 | Conference Paper | IST-REx-ID: 911
Probabilistic image colorization
A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, n.d.
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A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, n.d.
2017 | Conference Paper | IST-REx-ID: 1000
PixelCNN models with auxiliary variables for natural image modeling
A. Kolesnikov, C. Lampert, in:, Omnipress, 2017, pp. 1905–1914.
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A. Kolesnikov, C. Lampert, in:, Omnipress, 2017, pp. 1905–1914.
2017 | Conference Paper | IST-REx-ID: 998
iCaRL: Incremental classifier and representation learning
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
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S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
2016 | Conference Paper | IST-REx-ID: 1369
Seed, expand and constrain: Three principles for weakly-supervised image segmentation
A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
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A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
2016 | Conference Paper | IST-REx-ID: 1102
Improving weakly-supervised object localization by micro-annotation
A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
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A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
2014 | Conference Paper | IST-REx-ID: 2171
Closed-form approximate CRF training for scalable image segmentation
A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 550–565.
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A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 550–565.
8 Publications
2018 | Journal Article | IST-REx-ID: 563
Estimating barriers to gene flow from distorted isolation-by-distance patterns
H. Ringbauer, A. Kolesnikov, D. Field, N.H. Barton, Genetics 208 (2018) 1231–1245.
View
| Files available
| DOI
| Download (ext.)
H. Ringbauer, A. Kolesnikov, D. Field, N.H. Barton, Genetics 208 (2018) 1231–1245.
2018 | Thesis | IST-REx-ID: 197
Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, IST Austria, 2018.
View
| Files available
| DOI
A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, IST Austria, 2018.
2017 | Conference Paper | IST-REx-ID: 911
Probabilistic image colorization
A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, n.d.
View
| Download (ext.)
A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, n.d.
2017 | Conference Paper | IST-REx-ID: 1000
PixelCNN models with auxiliary variables for natural image modeling
A. Kolesnikov, C. Lampert, in:, Omnipress, 2017, pp. 1905–1914.
View
| Download (ext.)
A. Kolesnikov, C. Lampert, in:, Omnipress, 2017, pp. 1905–1914.
2017 | Conference Paper | IST-REx-ID: 998
iCaRL: Incremental classifier and representation learning
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
View
| DOI
| Download (ext.)
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
2016 | Conference Paper | IST-REx-ID: 1369
Seed, expand and constrain: Three principles for weakly-supervised image segmentation
A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
View
| DOI
| Download (ext.)
A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
2016 | Conference Paper | IST-REx-ID: 1102
Improving weakly-supervised object localization by micro-annotation
A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
View
| DOI
| Download (ext.)
A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
2014 | Conference Paper | IST-REx-ID: 2171
Closed-form approximate CRF training for scalable image segmentation
A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 550–565.
View
| DOI
| Download (ext.)
A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 550–565.