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 …

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

Few-shot learning aims to train a model with a limited number of base class samples to
classify the novel class samples. However, to attain generalization with a limited number of …

Image entropy is the metric used to represent a complexity of an image. This study considers
the hypothesis that image entropy differences affect machine learning algorithms' …

The few-shot learning paradigm aims to generalize to unseen tasks with limited samples.
However, a focus solely on class-level discrimination may fall short of achieving robust …

Recent advancements have significantly improved the efficiency and effectiveness of deep
learning methods for image-based remote sensing tasks. However, the requirement for large …

Few-shot segmentation (FSS) aims to segment novel classes given a small number of
labeled samples. Most of the existing studies do not fine-tune the model during meta-testing …

The vitality of a city is shaped by its social structure, environmental quality, and spatial form,
with child-friendliness being an essential component of urban vitality. While there are …

Deep neural networks in computer vision have shown remarkable progress in recognizing
facial genetic syndromes. Many genetic syndromes are difficult to detect, even for …

Abstract Hierarchical Few-shot Learning (HFSL) is a practical research of recognizing new
categories from insufficient samples, which leverages multi-grained knowledge among …