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 …

Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object
classification, segmentation and detection is often laborious owing to the irregular structure …

Few-shot learning (FSL) is an important and topical problem in computer vision that has
motivated extensive research into numerous methods spanning from sophisticated meta …

The ability to quickly learn a new task with minimal instruction-known as few-shot learning-is
a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot …

It is assumed that pre-training provides the feature extractor with strong class transferability
and that high novel class generalization can be achieved by simply reusing the transferable …

Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP and ALIGN, have
introduced a new paradigm for learning transferable visual representations. Recently, there …

Animal behavior is a critical aspect for a better understanding and management of animal
health and welfare. The combination of cameras with artificial intelligence holds significant …

In this paper we look at cross-domain few-shot classification which presents the challenging
task of learning new classes in previously unseen domains with few labelled examples …

Few shot classification aims to learn to recognize novel categories using only limited
samples per category. Most current few shot methods use a base dataset rich in labeled …

Computer vision is an interesting tool for animal behavior monitoring, mainly because it
limits animal handling and it can be used to record various traits using only one sensor …