32 citations · 109 across the 15 of their papers we have counts for
18 papers
Adam SLAM - the last mile of camera calibration with 3DGS
Matthieu Gendrin, Stéphane Pateux, Xiaoran Jiang +2
The quality of the camera calibration is of major importance for evaluating progresses in novel view synthesis, as a 1-pixel error on the calibration has a significant impact on th…
BOGausS: Better Optimized Gaussian Splatting
Stéphane Pateux, Matthieu Gendrin, Luce Morin +2
3D Gaussian Splatting (3DGS) proposes an efficient solution for novel view synthesis. Its framework provides fast and high-fidelity rendering. Although less complex than other solu…
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
Yuqing Hu, Stéphane Pateux, Vincent Gripon
Transductive Few-Shot learning has gained increased attention nowadays considering the cost of data annotations along with the increased accuracy provided by unlabelled samples in…
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Yassir Bendou, Yuqing Hu, Raphael Lafargue +4
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 lab…
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
Yuqing Hu, Vincent Gripon, Stéphane Pateux
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples. In the past few years, many methods have been proposed with the common…
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
Yuqing Hu, Vincent Gripon, Stéphane Pateux
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples. In the past few years, many methods have been proposed to solve few-sh…