Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared
with inductive few-shot learning, transductive models typically perform better as they …

Few-shot classification aims at leveraging knowledge learned in a deep learning model, in
order to obtain good classification performance on new problems, where only a few labeled …

Abstract Transductive Few-Shot Learning (TFSL) has recently attracted increasing attention
since it typically outperforms its inductive peer by leveraging statistics of query samples …

Distance-based classification is frequently used in transductive few-shot learning (FSL).
However, due to the high-dimensionality of image representations, FSL classifiers are prone …

This paper introduces a generalized few-shot segmentation framework with a
straightforward training process and an easy-to-optimize inference phase. In particular, we …

Abstract We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, ie classifying
instances among a set of classes for which we only have a few labeled samples, while …

Abstract We introduce a Parametric Information Maximization (PIM) model for the
Generalized Category Discovery (GCD) problem. Specifically, we propose a bi-level …

Transductive inference has been widely investigated in few-shot image classification but
completely overlooked in the recent fast growing literature on adapting vision-langage …

Abstract Transductive Few-Shot learning has gained increased attention nowadays
considering the cost of data annotations along with the increased accuracy provided by …

Few-shot Learning (FSL) aims to learn and distinguish new categories from a scant number
of available samples, presenting a significant challenge in the realm of deep learning …