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

Few-shot learners aim to recognize new object classes based on a small number of labeled
training examples. To prevent overfitting, state-of-the-art few-shot learners use meta …

In few-shot learning scenarios, the challenge is to generalize and perform well on new
unseen examples when only very few labeled examples are available for each task. Model …

Few-shot learning is an important area of research. Conceptually, humans are readily able
to understand new concepts given just a few examples, while in more pragmatic terms …

Computer vision tasks such as image classification, image retrieval, and few-shot learning
are currently dominated by Euclidean and spherical embeddings so that the final decisions …

We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any
feature embedding learned from the base classes, we minimize a quadratic binary …

Metric-based meta-learning techniques have successfully been applied to few-shot
classification problems. In this paper, we propose to leverage cross-modal information to …

Few-shot learning (FSL) has attracted increasing attention in recent years but remains
challenging, due to the intrinsic difficulty in learning to generalize from a few examples. This …

Despite its popularity, several recent works question the effectiveness of MAML when test
tasks are different from training tasks, thus suggesting various task-conditioned methodology …

The objective of few-shot learning is to design a system that can adapt to a given task with
only few examples while achieving generalization. Model-agnostic meta-learning (MAML) …