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Found 10 papers, 5 papers with code
LLM meets Vision-Language Models for Zero-Shot One-Class Classification
In this setting, only the label of the target class is available, and the goal is to discriminate between positive and negative query samples without requiring any validation example from the target task.
Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding
In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision.
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
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.
A Strong and Simple Deep Learning Baseline for BCI MI Decoding
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI.
Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach
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.
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
BCI Motor Imagery datasets usually are small and have different electrodes setups.
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
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
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.
Ranked #1 on
Few-Shot Learning
on Mini-Imagenet 5-way (1-shot)
Few-shot Decoding of Brain Activation Maps
Few-shot learning addresses problems for which a limited number of training examples are available.
Gradients of Connectivity as Graph Fourier Bases of Brain Activity
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.
