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 Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

The frequent outbreak of global infectious diseases has prompted the development of rapid
and effective diagnostic tools for the early screening of potential patients in point-of-care …

Most domain adaptation (DA) methods in cross-scene hyperspectral image classification
focus on cases where source data (SD) and target data (TD) with the same classes are …

In this paper, we address the few-shot classification task from a new perspective of optimal
matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to …

Conventional methods for object detection typically require a substantial amount of training
data and preparing such high-quality training data is very labor-intensive. In this paper, we …

Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

We show that the way inference is performed in few-shot segmentation tasks has a
substantial effect on performances--an aspect often overlooked in the literature in favor of …