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 …

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 …

Meta-learning aims to use the knowledge from previous tasks to facilitate the learning of
novel tasks. Many meta-learning models elaborately design various task-shared inductive …

Few-shot image classification aims to learn unseen classes with only a few training samples
for each class. However, most existing models still suffer from weak feature representation …

Data augmentation is crucial in training deep models, preventing them from overfitting to
limited data. Common data augmentation methods are effective, but recent advancements in …

Recently, the field of few-shot detection within remote sensing imagery has witnessed
significant advancements. Despite these progresses, the capacity for continuous conceptual …