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 …

Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

There is a growing demand for detailed and accurate landslide maps and inventories
around the globe, but particularly in hazard-prone regions such as the Himalayas. Most …

The increasing application of Artificial Intelligence (AI) in health and medicine has attracted
a great deal of research interest in recent decades. This study aims to provide a global and …

Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

The ability to quickly recognize and learn new visual concepts from limited samples enable
humans to quickly adapt to new tasks and environments. This ability is enabled by the …

We present PointAugment, a new auto-augmentation framework that automatically optimizes
and augments point cloud samples to enrich the data diversity when we train a classification …

This paper presents a hardness-aware deep metric learning (HDML) framework. Most
previous deep metric learning methods employ the hard negative mining strategy to …

Graph property prediction tasks are important and numerous. While each task offers a small
size of labeled examples, unlabeled graphs have been collected from various sources and …

Transfer learning is a machine learning technique that uses previously acquired knowledge
from a source domain to enhance learning in a target domain by reusing learned weights …