Jules Bourcier
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View article: Evaluating the label efficiency of contrastive self-supervised learning for multi-resolution satellite imagery
Evaluating the label efficiency of contrastive self-supervised learning for multi-resolution satellite imagery Open
The application of deep neural networks to remote sensing imagery is often\nconstrained by the lack of ground-truth annotations. Adressing this issue\nrequires models that generalize efficiently from limited amounts of labeled\ndata, allow…
View article: Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection
Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection Open
In defense-related remote sensing applications, such as vehicle detection on satellite imagery, supervised learning requires a huge number of labeled examples to reach operational performances. Such data are challenging to obtain as it req…
View article: Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery Open
The application of deep neural networks to remote sensing imagery is often constrained by the lack of ground-truth annotations. Adressing this issue requires models that generalize efficiently from limited amounts of labeled data, allowing…