In scenarios where sample collection is limited, studying few-shot learning algorithms such
as prototypical networks (P-Net) is a keynote topic for supervised multiple tree species …

Conventional training of deep neural networks requires a large number of the annotated
image which is a laborious and time-consuming task, particularly for rare objects. Few-shot …

Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks
without sufficient annotations. Despite the emergence of a number of few-shot learning …

Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of
images (eg, birds, flowers, and airplanes) belonging to different subclasses of the same …

**年来, 借助大规模数据集和庞大的计算资源, 以深度学**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 …