In recent years, the field of few-shot action recognition (FSAR) has garnered significant
attention. Although many methods primarily rely on mono-modal data, there is a growing …

Few-shot classification (FSC) is a challenging problem, which aims to identify novel classes
with limited samples. Most existing methods employ vanilla transfer learning or episodic …

Deep-learning-based synthetic aperture radar (SAR) automatic target recognition (ATR)
algorithms have achieved outstanding performance under the condition of hundreds or …

Few-shot learning is used in the development of models that can acquire novel class
concepts from limited training samples, facilitating rapid adaptation to novel, intricate, and …

Few-Shot Learning (FSL) leverages prior knowledge and generalization strategies to quickly
adapt to new tasks or recognize new objects with minimal input. Recently, CLIP-based …

Recently, few-shot action recognition has progressed significantly, as it has learned the
feature discriminability and designed suitable comparison methods. Still, there are the …

Few-shot action recognition is an emerging field in computer vision, primarily focused on
meta-learning within the same domain. However, challenges arise in real-world scenario …

With limited labeled samples, few-shot classification poses a challenge to standard deep
models and has attracted a surge of concern. Metric learning based approaches stand out …

In the research field of few-shot learning, the main difference between image-based and
video-based is the additional temporal dimension. In recent years, some works have used …

Few-shot classification (FSC) is a highly challenging task, as only a small number of labeled
samples are available when identifying new categories. Distance metric learning-based …