687 citations · 835 across the 19 of their papers we have counts for
11 papers · 1 filter
Semantic Generative Augmentations for Few-Shot Counting
Perla Doubinsky, Nicolas Audebert, Michel Crucianu +1
With the availability of powerful text-to-image diffusion models, recent works have explored the use of synthetic data to improve image classification performances. These works sho…
Optimization of Rank Losses for Image Retrieval
Elias Ramzi, Nicolas Audebert, Clément Rambour +3
In image retrieval, standard evaluation metrics rely on score ranking, \eg average precision (AP), recall at k (R@k), normalized discounted cumulative gain (NDCG). In this work we…
Wasserstein Loss for Semantic Editing in the Latent Space of GANs
Perla Doubinsky, Nicolas Audebert, Michel Crucianu +1
The latent space of GANs contains rich semantics reflecting the training data. Different methods propose to learn edits in latent space corresponding to semantic attributes, thus a…
Hierarchical Average Precision Training for Pertinent Image Retrieval
Elias Ramzi, Nicolas Audebert, Nicolas Thome +2
Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity. This…
Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM tree
Chen Dang, Hicham Randrianarivo, Raphaël Fournier-S'Niehotta +1
Web Image Context Extraction (WICE) consists in obtaining the textual information describing an image using the content of the surrounding webpage. A common preprocessing step befo…
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch +2
The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms. Indeed, raw image data are abundant whi…
Multimodal deep networks for text and image-based document classification
Nicolas Audebert, Catherine Herold, Kuider Slimani +1
Classification of document images is a critical step for archival of old manuscripts, online subscription and administrative procedures. Computer vision and deep learning have been…
Deep learning for semantic segmentation of remote sensing images with rich spectral content
A Hamida, A. Benoît, P. Lambert +4
With the rapid development of Remote Sensing acquisition techniques, there is a need to scale and improve processing tools to cope with the observed increase of both data volume an…
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images. Deep neural networks have been used in the past for remote sensing dat…
How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we…
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the…