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 …

Current face forgery detection methods achieve high accuracy under the within-database
scenario where training and testing forgeries are synthesized by the same algorithm …

Existing few-shot segmentation methods have achieved great progress based on the
support-query matching framework. But they still heavily suffer from the limited coverage of …

We propose to address the problem of few-shot classification by meta-learning" what to
observe" and" where to attend" in a relational perspective. Our method leverages relational …

Few-shot semantic segmentation aims at learning to segment a target object from a query
image using only a few annotated support images of the target class. This challenging task …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms
that can continually learn new concepts from a few data points, without forgetting knowledge …

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 …

Diffusion models have achieved promising results in image restoration tasks, yet suffer from
time-consuming, excessive computational resource consumption, and unstable restoration …