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 …

Environmental changes can profoundly impact the performance of artificial intelligence
systems operating in the real world, with effects ranging from overt catastrophic failures to …

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 …

In this paper, we look at the problem of cross-domain few-shot classification that aims to
learn a classifier from previously unseen classes and domains with few labeled samples …

Abstract" A picture is worth a thousand words", significantly beyond mere a categorization.
Accompanied by that, many patches of the image could have completely irrelevant …

Few-shot classification is a promising approach to solving the problem of classifying novel
classes with only limited annotated data for training. Existing methods based on meta …

The goal of few-shot learning is to learn the generalizability from seen to unseen data with
only a few samples. Most previous few-shot learning focus on learning generalizability …

Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic
model for capturing task-specific knowledge of the test task, rely only on few-labeled support …

Few-shot classification is a challenging problem that aims to learn a model that can adapt to
unseen classes given a few labeled samples. Recent approaches pre-train a feature …

Aiming at the existing problems of metric-based methods, there are problems such as
inadequate feature extraction, inaccurate class feature representation, and single similarity …