Alexander Kolesnikov

Lampert Group

9 Publications

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[9]
2019 | Conference Paper | IST-REx-ID: 7640 | OA
Detecting visual relationships using box attention
A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[8]
2018 | Thesis | IST-REx-ID: 197 | OA
Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
[7]
2018 | Journal Article | IST-REx-ID: 563 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS
 
[6]
2017 | Conference Paper | IST-REx-ID: 1000 | OA
PixelCNN models with auxiliary variables for natural image modeling
A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine Learning, JMLR, 2017, pp. 1905–1914.
[Submitted Version] View | Download Submitted Version (ext.) | WoS | arXiv
 
[5]
2017 | Conference Paper | IST-REx-ID: 998 | OA
iCaRL: Incremental classifier and representation learning
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[4]
2017 | Conference Paper | IST-REx-ID: 911 | OA
Probabilistic image colorization
A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12.
[Published Version] View | Files available | DOI | arXiv
 
[3]
2016 | Conference Paper | IST-REx-ID: 1102 | OA
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.
[Published Version] View | DOI | Download Published Version (ext.)
 
[2]
2016 | Conference Paper | IST-REx-ID: 1369 | OA
Seed, expand and constrain: Three principles for weakly-supervised image segmentation
A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
[Preprint] View | DOI | Download Preprint (ext.)
 
[1]
2014 | Conference Paper | IST-REx-ID: 2171 | OA
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.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
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9 Publications

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[9]
2019 | Conference Paper | IST-REx-ID: 7640 | OA
Detecting visual relationships using box attention
A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[8]
2018 | Thesis | IST-REx-ID: 197 | OA
Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
[7]
2018 | Journal Article | IST-REx-ID: 563 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS
 
[6]
2017 | Conference Paper | IST-REx-ID: 1000 | OA
PixelCNN models with auxiliary variables for natural image modeling
A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine Learning, JMLR, 2017, pp. 1905–1914.
[Submitted Version] View | Download Submitted Version (ext.) | WoS | arXiv
 
[5]
2017 | Conference Paper | IST-REx-ID: 998 | OA
iCaRL: Incremental classifier and representation learning
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[4]
2017 | Conference Paper | IST-REx-ID: 911 | OA
Probabilistic image colorization
A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12.
[Published Version] View | Files available | DOI | arXiv
 
[3]
2016 | Conference Paper | IST-REx-ID: 1102 | OA
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.
[Published Version] View | DOI | Download Published Version (ext.)
 
[2]
2016 | Conference Paper | IST-REx-ID: 1369 | OA
Seed, expand and constrain: Three principles for weakly-supervised image segmentation
A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
[Preprint] View | DOI | Download Preprint (ext.)
 
[1]
2014 | Conference Paper | IST-REx-ID: 2171 | OA
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.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
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