Abstract Vision Transformers (ViTs) emerge to achieve impressive performance on many
data-abundant computer vision tasks by capturing long-range dependencies among local …

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 …

近年来, 借助大规模数据集和庞大的计算资源, 以深度学习为代表的人工智能算法在诸多领域
取得成功. 其中计算机视觉领域的图像分类技术蓬勃发展, 并涌现出许多成熟的视觉任务分类 …

Few-shot classification (FSC) entails learning novel classes given only a few examples per
class after a pre-training (or meta-training) phase on a set of base classes. Recent works …

Abstract Models for fine-grained image classification tasks, where the difference between
some classes can be extremely subtle and the number of samples per class tends to be low …

Since many pre-trained vision transformers emerge and provide strong representation for
various downstream tasks, we aim to adapt them to few-shot image classification tasks in …

Few-shot image classification, whose goal is to generalize to unseen tasks with scarce
labeled data, has developed rapidly over the years. However, in traditional few-shot learning …

State-of-the-art image models predominantly follow a two-stage strategy: pre-training on
large datasets and fine-tuning with cross-entropy loss. Many studies have shown that using …

Learning from a limited amount of data namely Few-Shot Learning stands out as a
challenging computer vision task. Several works exploit semantics and design complicated …

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 …