Search Results for author: Giulia Lioi

Found 8 papers, 5 papers with code

Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning

1 code implementation24 Nov 2023 Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene

In this paper, we present a novel approach that leverages text-derived statistics to predict the mean and covariance of the visual feature distribution for each class.

Few-Shot Learning

A Strong and Simple Deep Learning Baseline for BCI MI Decoding

1 code implementation11 Sep 2023 Yassine El Ouahidi, Vincent Gripon, Bastien Pasdeloup, Ghaith Bouallegue, Nicolas Farrugia, Giulia Lioi

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI.

EEG Transfer Learning

Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach

1 code implementation16 Jan 2023 Yassir Bendou, Lucas Drumetz, Vincent Gripon, Giulia Lioi, Bastien Pasdeloup

Then, we introduce a downstream classifier meant to exploit the presence of multiple objects to improve the performance of few-shot classification, in the case of extreme settings where only one shot is given for its class.

Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding

no code implementations9 Mar 2022 Yassine El Ouahidi, Hugo Tessier, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon

In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals.

EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients

2 code implementations24 Jan 2022 Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, Stéphane Pateux, Vincent Gripon

Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available.

Few-Shot Image Classification Few-Shot Learning

Few-shot Decoding of Brain Activation Maps

1 code implementation23 Oct 2020 Myriam Bontonou, Giulia Lioi, Nicolas Farrugia, Vincent Gripon

Few-shot learning addresses problems for which a limited number of training examples are available.

Few-Shot Learning

Gradients of Connectivity as Graph Fourier Bases of Brain Activity

no code implementations26 Sep 2020 Giulia Lioi, Vincent Gripon, Abdelbasset Brahim, François Rousseau, Nicolas Farrugia

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neuroscience.

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