Most existing few-shot classification methods only consider generalization on one dataset
(ie, single-domain), failing to transfer across various seen and unseen domains. In this …

Restricted by observation conditions, some scarce targets in the synthetic aperture radar
(SAR) image only have a few samples, making effective classification a challenging task …

Deep learning-based methods have demonstrated exceptional performance in the field of
synthetic aperture radar automatic target recognition (SAR ATR). However, obtaining a …

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 …

Traditional fine-grained image classification generally requires abundant labeled samples to
deal with the low inter-class variance but high intra-class variance problem. However, in …

Few-shot classification studies the problem of quickly adapting a deep learner to
understanding novel classes based on few support images. In this context, recent research …

Few-shot fine-grained recognition is an attractive research topic that aims to differentiate
between sub-categories using a limited number of labeled examples. Due to the …

Training a generalized reliable model is a great challenge since sufficiently labeled data are
unavailable in some open application scenarios. Few-shot learning (FSL) aims to learn new …

Unknown image deformation and few-shot issues have posed significant challenges to
inverse synthetic aperture radar (ISAR) target classification. To achieve robust feature …

Enabling effective learning using only a few presented examples is a crucial but difficult
computer vision objective. Few-shot learning have been proposed to address the …