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 
LibreCat: https://research-explorer.ista.ac.at/record/7640
DOI: 10.1109/ICCVW.2019.00217
WoS: 000554591601098
arXiv: 1807.02136

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 
LibreCat: https://research-explorer.ista.ac.at/record/197
DOI: 10.15479/AT:ISTA:th_1021

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 
LibreCat: https://research-explorer.ista.ac.at/record/563
DOI: 10.1534/genetics.117.300638
WoS: 000426219600025

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.
LibreCat: https://research-explorer.ista.ac.at/record/1000
WoS: 000683309501102
arXiv: 1612.08185

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 
LibreCat: https://research-explorer.ista.ac.at/record/998
DOI: 10.1109/CVPR.2017.587
WoS: 000418371405066

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 
LibreCat: https://research-explorer.ista.ac.at/record/911
DOI: 10.5244/c.31.85
arXiv: 1705.04258

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 
LibreCat: https://research-explorer.ista.ac.at/record/1102
DOI: 10.5244/C.30.92

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 
LibreCat: https://research-explorer.ista.ac.at/record/1369
DOI: 10.1007/978-3-319-46493-0_42

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 
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 10.1007/978-3-319-10578-9_36