Benjamin Kellenberger
Cited by
Cited by
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
B Bhushan Damodaran, B Kellenberger, R Flamary, D Tuia, N Courty
Proceedings of the European Conference on Computer Vision (ECCV), 447-463, 2018
Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning
B Kellenberger, D Marcos, Diego, 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, 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
Scale equivariance in CNNs with vector fields
D Marcos, B Kellenberger, S Lobry, D Tuia
arXiv preprint arXiv:1807.11783, 2018
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
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
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 Conference on Computer Vision and Pattern …, 2019
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
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
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
Best Practices to Train Deep Models on Imbalanced Datasets—A Case Study on Animal Detection in Aerial Imagery
B Kellenberger, D Marcos, D Tuia
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018
Detecting Animals in Repeated UAV Image Acquisitions by Matching CNN Activations with Optimal Transport
B Kellenberger, D Marcos, N Courty, D Tuia
IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018
Counting using deep learning regression gives value to ecological surveys
J Hoekendijk, B Kellenberger, G Aarts, S Brasseur, SSH Poiesz, D Tuia
Scientific reports 11 (1), 1-12, 2021
Deep Domain Adaptation in Earth Observation
B Kellenberger, O Tasar, B Bhushan Damodaran, N Courty, D Tuia
Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021
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
Mapping Vulnerable Populations with AI
B Kellenberger, JE Vargas-Muñoz, D Tuia, RC Daudt, K Schindler, ...
arXiv preprint arXiv:2107.14123, 2021
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