Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Few-shot image classification is a challenging problem that aims to achieve the human level
of recognition based only on a small number of training images. One main solution to few …

This paper presents a simple yet effective approach to modeling space-time
correspondences in the context of video object segmentation. Unlike most existing …

Object recognition requires a generalization capability to avoid overfitting, especially when
the samples are extremely few. Generalization from limited samples, usually studied under …

In this paper we reformulate few-shot classification as a reconstruction problem in latent
space. The ability of the network to reconstruct a query feature map from support features of …

In image classification, it is common practice to train deep networks to extract a single
feature vector per input image. Few-shot classification methods also mostly follow this trend …

Enhancing images in low-light scenes is a challenging but widely concerned task in the
computer vision. The mainstream learning-based methods mainly acquire the enhanced …

Few-shot learning is an important area of research. Conceptually, humans are readily able
to understand new concepts given just a few examples, while in more pragmatic terms …

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 …

Abstract Few-shot learning (FSL), which aims to recognise new classes by adapting the
learned knowledge with extremely limited few-shot (support) examples, remains an …