14 citations · 21 across the 14 of their papers we have counts for
6 papers · 1 filter
Burnt area extraction from high-resolution satellite images based on anomaly detection
Oscar David Rafael Narvaez Luces, Minh-Tan Pham, Quentin Poterek +1
Wildfire detection using satellite images is a widely studied task in remote sensing with many applications to fire delineation and mapping. Recently, deep learning methods have be…
Knowledge Distillation for Object Detection: from generic to remote sensing datasets
Hoàng-Ân Lê, Minh-Tan Pham
Knowledge distillation, a well-known model compression technique, is an active research area in both computer vision and remote sensing communities. In this paper, we evaluate in a…
Multimodal Object Detection in Remote Sensing
Abdelbadie Belmouhcine, Jean-Christophe Burnel, Luc Courtrai +2
Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area…
Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder
Minh-Tan Pham, Hugo Gangloff, Sébastien Lefèvre
This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments. Such an approach leverages an anomaly detection…
Object counting from aerial remote sensing images: application to wildlife and marine mammals
Tanya Singh, Hugo Gangloff, Minh-Tan Pham
Anthropogenic activities pose threats to wildlife and marine fauna, prompting the need for efficient animal counting methods. This research study utilizes deep learning techniques…
Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification
Paul Berg, Minh-Tan Pham, Nicolas Courty
With the current ubiquity of deep learning methods to solve computer vision and remote sensing specific tasks, the need for labelled data is growing constantly. However, in many ca…