Recent image generation models such as Stable Diffusion have exhibited an impressive
ability to generate fairly realistic images starting from a simple text prompt. Could such …

Few-shot object detection, the problem of modelling novel object detection categories with
few training instances, is an emerging topic in the area of few-shot learning and object …

Few-shot image classification aims at exploring transferable features from base classes to
recognize images of the unseen novel classes with only a few labeled images. Existing …

In recent years, few-shot learning has received increasing attention in the Internet of Things
areas. Few-shot learning aims to distinguish unseen classes with a few labeled samples …

Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual
samples. Existing approaches attempt to incorporate semantic information into the limited …

An effective way for few-shot learning (FSL) is to establish a metric space where the distance
between a query and the prototype of each class is computed for classification, and the key …

We introduce HuTuMotion, an innovative approach for generating natural human motions
that navigates latent motion diffusion models by leveraging few-shot human feedback …

Learning novel object concepts from limited samples remains a considerable challenge in
deep learning. The main directions for improving the few-shot learning models include (i) …

Data augmentation is a popular way for few-shot learning (FSL). It generates more samples
as supplements and then transforms the FSL task into a common supervised learning …

Learning in hyperbolic spaces has attracted growing attention recently, owing to their
capabilities in capturing hierarchical structures of data. However, existing learning …