Few-shot image classification is a challenging topic in pattern recognition and computer
vision. Few-shot fine-grained image classification is even more challenging, due to not only …

The fine-grained few-shot classification is a challenging task in computer vision, aiming to
classify images with subtle and detailed differences given scarce labeled samples. A …

The collection and annotation of large-scale video data pose significant challenges,
prompting the exploration of few-shot models to recognize unseen actions with limited …

In the context of the complexities of fine-grained image classification intertwined with the
constraints of few-shot learning, this paper focuses on overcoming the challenges posed by …

The main challenge for Few-Shot Fine-Grained (FSFG) image classification is to learn
discriminative feature representations with few labeled samples. In response to this …

In few-shot fine-grained recognition (FS-FGR) tasks, the main challenge is to distinguish
novel categories with high intra-class variations and low inter-class differences given scarce …

Few-shot image classification aims to learn novel classes with limited labeled samples for
each class. Recent research mainly focuses on reconstructing a query image from a support …

Few-shot fine-grained image classification (FSFGIC) aims to classify subspecies with similar
appearances under conditions of very limited data. In this paper, we observe an interesting …

Few-shot classification (FSC) is a fundamental yet challenging task in computer vision that
involves recognizing novel classes from limited data. While previous methods have focused …

Few-shot learning (FSL) aims to accurately classify samples of different categories using
very limited training data. In this work, we thoroughly find that existing FSL methods ignore …