Alexander Kolesnikov

Lampert Group

9 Publications

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[9]
2019 | Conference Paper | IST-REx-ID: 7640 | OA
Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting visual relationships using box attention. In Proceedings of the 2019 International Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[8]
2018 | Thesis | IST-REx-ID: 197 | OA
Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_1021
[Published Version] View | Files available | DOI
 
[7]
2018 | Journal Article | IST-REx-ID: 563 | OA
Ringbauer, H., Kolesnikov, A., Field, D., & Barton, N. H. (2018). Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.117.300638
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS
 
[6]
2017 | Conference Paper | IST-REx-ID: 1000 | OA
Kolesnikov, A., & Lampert, C. (2017). PixelCNN models with auxiliary variables for natural image modeling. In 34th International Conference on Machine Learning (Vol. 70, pp. 1905–1914). Sydney, Australia: JMLR.
[Submitted Version] View | Download Submitted Version (ext.) | WoS | arXiv
 
[5]
2017 | Conference Paper | IST-REx-ID: 998 | OA
Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. (2017). iCaRL: Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.587
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[4]
2017 | Conference Paper | IST-REx-ID: 911 | OA
Royer, A., Kolesnikov, A., & Lampert, C. (2017). Probabilistic image colorization (p. 85.1-85.12). Presented at the BMVC: British Machine Vision Conference, London, United Kingdom: BMVA Press. https://doi.org/10.5244/c.31.85
[Published Version] View | Files available | DOI | arXiv
 
[3]
2016 | Conference Paper | IST-REx-ID: 1102 | OA
Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object localization by micro-annotation. In Proceedings of the British Machine Vision Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom: BMVA Press. https://doi.org/10.5244/C.30.92
[Published Version] View | DOI | Download Published Version (ext.)
 
[2]
2016 | Conference Paper | IST-REx-ID: 1369 | OA
Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711). Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42
[Preprint] View | DOI | Download Preprint (ext.)
 
[1]
2014 | Conference Paper | IST-REx-ID: 2171 | OA
Kolesnikov, A., Guillaumin, M., Ferrari, V., & Lampert, C. (2014). Closed-form approximate CRF training for scalable image segmentation. 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) (Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10578-9_36
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
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9 Publications

Mark all

[9]
2019 | Conference Paper | IST-REx-ID: 7640 | OA
Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting visual relationships using box attention. In Proceedings of the 2019 International Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[8]
2018 | Thesis | IST-REx-ID: 197 | OA
Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_1021
[Published Version] View | Files available | DOI
 
[7]
2018 | Journal Article | IST-REx-ID: 563 | OA
Ringbauer, H., Kolesnikov, A., Field, D., & Barton, N. H. (2018). Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.117.300638
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS
 
[6]
2017 | Conference Paper | IST-REx-ID: 1000 | OA
Kolesnikov, A., & Lampert, C. (2017). PixelCNN models with auxiliary variables for natural image modeling. In 34th International Conference on Machine Learning (Vol. 70, pp. 1905–1914). Sydney, Australia: JMLR.
[Submitted Version] View | Download Submitted Version (ext.) | WoS | arXiv
 
[5]
2017 | Conference Paper | IST-REx-ID: 998 | OA
Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. (2017). iCaRL: Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.587
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[4]
2017 | Conference Paper | IST-REx-ID: 911 | OA
Royer, A., Kolesnikov, A., & Lampert, C. (2017). Probabilistic image colorization (p. 85.1-85.12). Presented at the BMVC: British Machine Vision Conference, London, United Kingdom: BMVA Press. https://doi.org/10.5244/c.31.85
[Published Version] View | Files available | DOI | arXiv
 
[3]
2016 | Conference Paper | IST-REx-ID: 1102 | OA
Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object localization by micro-annotation. In Proceedings of the British Machine Vision Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom: BMVA Press. https://doi.org/10.5244/C.30.92
[Published Version] View | DOI | Download Published Version (ext.)
 
[2]
2016 | Conference Paper | IST-REx-ID: 1369 | OA
Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711). Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42
[Preprint] View | DOI | Download Preprint (ext.)
 
[1]
2014 | Conference Paper | IST-REx-ID: 2171 | OA
Kolesnikov, A., Guillaumin, M., Ferrari, V., & Lampert, C. (2014). Closed-form approximate CRF training for scalable image segmentation. 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) (Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10578-9_36
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
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