429 citations · 911 across the 6 of their papers we have counts for
6 papers
Adaptive Compression-based Lifelong Learning
Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko +1
The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two ma…
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…
Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution
Shivangi Srivastava, John E. Vargas-Muñoz, Devis Tuia
Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and…
Correcting rural building annotations in OpenStreetMap using convolutional neural networks
John E. Vargas-Muñoz, Sylvain Lobry, Alexandre X. Falcão +1
Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap…
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
Michele Volpi, Devis Tuia
Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial da…
Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions
Devis Tuia, Rémi Flamary, Nicolas Courty
In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and…