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 …

Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Automated visual understanding of our diverse and open world demands computer vision
models to generalize well with minimal customization for specific tasks, similar to human …

Vision transformer (ViT) recently has drawn great attention in computer vision due to its
remarkable model capability. However, most prevailing ViT models suffer from huge number …

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 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 …

Self-supervised visual representation learning has seen huge progress recently, but no
large scale evaluation has compared the many models now available. We evaluate the …

We show that the way inference is performed in few-shot segmentation tasks has a
substantial effect on performances--an aspect often overlooked in the literature in favor of …

Abstract We introduce Domain-Adaptive Prompt (DAP), a novel method for continual
learning using Vision Transformers (ViT). Prompt-based continual learning has recently …

Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based
meta-learning algorithms. MAML learns new tasks with a few data samples using inner …