244 citations · 250 across the 10 of their papers we have counts for
5 papers · 1 filter
Open-Set Likelihood Maximization for Few-Shot Learning
Malik Boudiaf, Etienne Bennequin, Myriam Tami +4
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously de…
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Etienne Bennequin, Myriam Tami, Antoine Toubhans +1
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these cur…
Spatial Contrastive Learning for Few-Shot Classification
Yassine Ouali, Céline Hudelot, Myriam Tami
In this paper, we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent r…
Autoregressive Unsupervised Image Segmentation
Yassine Ouali, Céline Hudelot, Myriam Tami
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspirati…
Semi-Supervised Semantic Segmentation with Cross-Consistency Training
Yassine Ouali, Céline Hudelot, Myriam Tami
In this paper, we present a novel cross-consistency based semi-supervised approach for semantic segmentation. Consistency training has proven to be a powerful semi-supervised learn…