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 …

Meta-learning has gained wide popularity as a training framework that is more data-efficient
than traditional machine learning methods. However, its generalization ability in complex …

Recently few-shot segmentation (FSS) has been extensively developed. Most previous
works strive to achieve generalization through the meta-learning framework derived from …

Despite the progress made by few-shot segmentation (FSS) in low-data regimes, the
generalization capability of most previous works could be fragile when countering hard …

Conventional deep CNN-based segmentation approaches have achieved satisfactory
performance in recent years, however, they are essentially Big Data-driven technologies …

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL)
methods: the pre-trained knowledge is indeed a confounder that limits the performance. This …

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 …

The recent flourish of deep learning in various tasks is largely accredited to the rich and
accessible labeled data. Nonetheless, massive supervision remains a luxury for many real …

Few-shot learning, ie, learning novel concepts from few examples, is fundamental to
practical visual recognition systems. While most of existing work has focused on few-shot …

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 …