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 …

In recent years, with the development of science and technology, powerful computing
devices have been constantly developing. As an important foundation, deep learning (DL) …

Few-shot learning aims to train a model with a limited number of base class samples to
classify the novel class samples. However, to attain generalization with a limited number of …

Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes
with limited support examples. Existing methods based on Region Proposal Networks …

We study the topic of large-scale few-shot image recognition with semantic-visual relational
knowledge-based transfer learning. Compared with classical few-shot learning, which is …

Few-shot classification (FSC) is a challenging problem, which aims to identify novel classes
with limited samples. Most existing methods employ vanilla transfer learning or episodic …

Humans exhibit a remarkable ability to learn quickly from a limited number of labeled
samples, a capability that starkly contrasts with that of current machine learning systems …

Most few-shot learning works rely on the same domain assumption between the base and
the target tasks, hindering their practical applications. This paper proposes an adaptive …