Abstract Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new
classification tasks with a shift in task distribution. Understanding the difficulties posed by …

Recently, transfer learning has generated promising performance in few-shot classification
by pre-training a backbone network on base classes and then applying it to novel classes …

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 …

Transfer learning has been widely adopted for few-shot classification. Recent studies reveal
that obtaining good generalization representation of images on novel classes is the key to …

In recent computer vision research, the pursuit of improved classification performance often
leads to the adoption of complex, large-scale models. However, the actual deployment of …

Few-shot classification (FSC) has aroused increasing attentions over years, which attempts
to perform classification given a few labeled samples. In the context of transfer-learning for …