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 …

Abstract Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new
classification tasks with a shift in task distribution. Understanding the difficulties posed by …

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 …

Few-shot text classification has recently been promoted by the meta-learning paradigm
which aims to identify target classes with knowledge transferred from source classes with …

In recent years, with the development of science and technology, powerful computing
devices have been constantly developing. As an important foundation, deep learning (DL) …

In scenarios where sample collection is limited, studying few-shot learning algorithms such
as prototypical networks (P-Net) is a keynote topic for supervised multiple tree species …

Conventional training of deep neural networks requires a large number of the annotated
image which is a laborious and time-consuming task, particularly for rare objects. Few-shot …

Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks
without sufficient annotations. Despite the emergence of a number of few-shot learning …

Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of
images (eg, birds, flowers, and airplanes) belonging to different subclasses of the same …

Despite significant success of deep learning in object detection tasks, the standard training
of deep neural networks requires access to a substantial quantity of annotated images …