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 …

Deep learning methods have achieved significant results in various fields. Due to the
success of these methods, many researchers have used deep learning algorithms in …

Large language models have shown impressive few-shot results on a wide range of tasks.
However, when knowledge is key for such results, as is the case for tasks such as question …

Few-shot learning has become essential for producing models that generalize from few
examples. In this work, we identify that metric scaling and metric task conditioning are …

Abstract Research into Few-shot Semantic Segmentation (FSS) has attracted great attention,
with the goal to segment target objects in a query image given only a few annotated support …

Humans can quickly learn new visual concepts, perhaps because they can easily visualize
or imagine what novel objects look like from different views. Incorporating this ability to …

The rapid development of deep learning brings effective solutions for remote sensing image
interpretation. Training deep neural network models usually require a large number of …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Low-shot visual learning--the ability to recognize novel object categories from very few
examples--is a hallmark of human visual intelligence. Existing machine learning approaches …

The aim of this paper is to give an overview of domain adaptation and transfer learning with
a specific view on visual applications. After a general motivation, we first position domain …