Benjamin Kellenberger
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Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
BB Damodaran, B Kellenberger, R Flamary, D Tuia, N Courty
Proceedings of the European conference on computer vision (ECCV), 447-463, 2018
Perspectives in machine learning for wildlife conservation
D Tuia, B Kellenberger, S Beery, BR Costelloe, S Zuffi, B Risse, A Mathis, ...
Nature communications 13 (1), 1-15, 2022
Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning
B Kellenberger, D Marcos, D Tuia
Remote sensing of environment 216, 139-153, 2018
Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models
D Marcos, M Volpi, B Kellenberger, D Tuia
ISPRS journal of photogrammetry and remote sensing 145, 96-107, 2018
Learning deep structured active contours end-to-end
D Marcos, D Tuia, B Kellenberger, L Zhang, M Bai, R Liao, R Urtasun
Proceedings of the IEEE conference on computer vision and pattern …, 2018
Half a percent of labels is enough: Efficient animal detection in UAV imagery using deep CNNs and active learning
B Kellenberger, D Marcos, S Lobry, D Tuia
IEEE Transactions on Geoscience and Remote Sensing 57 (12), 9524-9533, 2019
Fast animal detection in UAV images using convolutional neural networks
B Kellenberger, M Volpi, D Tuia
2017 IEEE international geoscience and remote sensing symposium (IGARSS …, 2017
Deep learning for automated detection of Drosophila suzukii: potential for UAV‐based monitoring
PPJ Roosjen, B Kellenberger, L Kooistra, DR Green, J Fahrentrapp
Pest Management Science 76 (9), 2994-3002, 2020
Scale equivariance in CNNs with vector fields
D Marcos, B Kellenberger, S Lobry, D Tuia
arXiv preprint arXiv:1807.11783, 2018
21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning
B Kellenberger, T Veen, E Folmer, D Tuia
Remote Sensing in Ecology and Conservation 7 (3), 445-460, 2021
AIDE: Accelerating image‐based ecological surveys with interactive machine learning
B Kellenberger, D Tuia, D Morris
Methods in Ecology and Evolution 11 (12), 1716-1727, 2020
When a few clicks make all the difference: Improving weakly-supervised wildlife detection in UAV images
B Kellenberger, D Marcos, D Tuia
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
How to find a good image-text embedding for remote sensing visual question answering?
C Chappuis, S Lobry, B Kellenberger, BL Saux, D Tuia
arXiv preprint arXiv:2109.11848, 2021
Counting using deep learning regression gives value to ecological surveys
JPA Hoekendijk, B Kellenberger, G Aarts, S Brasseur, SSH Poiesz, D Tuia
Scientific reports 11 (1), 23209, 2021
Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning
TA Nguyen, B Kellenberger, D Tuia
Remote Sensing of Environment 281, 113217, 2022
Self-supervised pretraining and controlled augmentation improve rare wildlife recognition in UAV images
X Zheng, B Kellenberger, R Gong, I Hajnsek, D Tuia
Proceedings of the IEEE/CVF International Conference on Computer Vision, 732-741, 2021
Better generic objects counting when asking questions to images: A multitask approach for remote sensing visual question answering
S Lobry, D Marcos, B Kellenberger, D Tuia
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information …, 2020
A deep network approach to multitemporal cloud detection
D Tuia, B Kellenberger, A Pérez-Suey, G Camps-Valls
IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018
The wheel of design: Assessing and refining the usability of geoportals
B Kellenberger, I Iosifescu Enescu, R Nicola, CM Iosifescu Enescu, ...
International Journal of Cartography 2 (1), 95-112, 2016
Fine-grained population mapping from coarse census counts and open geodata
N Metzger, JE Vargas-Muñoz, RC Daudt, B Kellenberger, TTT Whelan, ...
Scientific Reports 12 (1), 20085, 2022
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