A two-stage training paradigm consisting of sequential pre-training and meta-training stages
has been widely used in current few-shot learning (FSL) research. Many of these methods …

The goal of few-shot learning is to learn a classifier that can recognize unseen classes from
limited support data with labels. A common practice for this task is to train a model on the …

Few-shot learning is a challenging problem since only a few examples are provided to
recognize a new class. Several recent studies exploit additional semantic information, eg …

In many real-world problems, collecting a large number of labeled samples is infeasible.
Few-shot learning (FSL) is the dominant approach to address this issue, where the objective …

Abstract" A picture is worth a thousand words", significantly beyond mere a categorization.
Accompanied by that, many patches of the image could have completely irrelevant …

The category gap between training and evaluation has been characterised as one of the
main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time …

Single image-level annotations only correctly describe an often small subset of an image's
content, particularly when complex real-world scenes are depicted. While this might be …

Few-shot Learning has been studied to mimic human visual capabilities and learn effective
models without the need of exhaustive human annotation. Even though the idea of meta …

Abstract Few-Shot Learning (FSL) aims at classifying samples into new unseen classes with
only a handful of labeled samples available. However, most of the existing methods are …

Graph neural networks (GNNs) have been used to tackle the few-shot learning (FSL)
problem and shown great potentials under the transductive setting. However under the …