143 citations · 194 across the 6 of their papers we have counts for
9 papers · 1 filter
How to find a good image-text embedding for remote sensing visual question answering?
Christel Chappuis, Sylvain Lobry, Benjamin Kellenberger +2
Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone. VQA considers a que…
Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images
Xiaochen Zheng, Benjamin Kellenberger, Rui Gong +2
Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on deep learning and thus require vast amounts…
Mapping Vulnerable Populations with AI
Benjamin Kellenberger, John E. Vargas-Muñoz, Devis Tuia +6
Humanitarian actions require accurate information to efficiently delegate support operations. Such information can be maps of building footprints, building functions, and populatio…
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning
Benjamin Kellenberger, Diego Marcos, Sylvain Lobry +1
We present an Active Learning (AL) strategy for re-using a deep Convolutional Neural Network (CNN)-based object detector on a new dataset. This is of particular interest for wildli…
Scale equivariance in CNNs with vector fields
Diego Marcos, Benjamin Kellenberger, Sylvain Lobry +1
We study the effect of injecting local scale equivariance into Convolutional Neural Networks. This is done by applying each convolutional filter at multiple scales. The output is a…
Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning
Benjamin Kellenberger, Diego Marcos, Devis Tuia
Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dan…