3 citations · 6 across the 7 of their papers we have counts for
9 papers
FSMODNet: A Closer Look at Few-Shot Detection in Multispectral Data
Manuel Nkegoum, Minh-Tan Pham, Élisa Fromont +2
Few-shot multispectral object detection (FSMOD) addresses the challenge of detecting objects across visible and thermal modalities with minimal annotated data. In this paper, we ex…
Robust Detection of Synthetic Tabular Data under Schema Variability
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
The rise of powerful generative models has sparked concerns over data authenticity. While detection methods have been extensively developed for images and text, the case of tabular…
Datum-wise Transformer for Synthetic Tabular Data Detection in the Wild
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
The growing power of generative models raises major concerns about the authenticity of published content. To address this problem, several synthetic content detection methods have…
Synthetic Tabular Data Detection In the Wild
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores wh…
Cross-table Synthetic Tabular Data Detection
G. Charbel N. Kindji, Lina Maria Rojas-Barahona, Elisa Fromont +1
Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores wh…
Tabular Data Generation Models: An In-Depth Survey and Performance Benchmarks with Extensive Tuning
G. Charbel N. Kindji, Lina Maria Rojas-Barahona, Elisa Fromont +1
The ability to train generative models that produce realistic, safe and useful tabular data is essential for data privacy, imputation, oversampling, explainability or simulation. H…
Mitigating analytical variability in fMRI results with style transfer
Elodie Germani, Camille Maumet, Elisa Fromont
We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption th…
The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis
Elodie Germani, Elisa Fromont, Pierre Maurel +1
Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability including differences due to: the sampling of the participants, diffe…
Uncovering communities of pipelines in the task-fMRI analytical space
Elodie Germani, Elisa Fromont, Camille Maumet
Analytical workflows in functional magnetic resonance imaging are highly flexible with limited best practices as to how to choose a pipeline. While it has been shown that the use o…