Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Few-shot learning (FSL) is an important and topical problem in computer vision that has
motivated extensive research into numerous methods spanning from sophisticated meta …

The annotations used during the training process are crucial for the inference results of
remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be …

Few-shot classification aims at leveraging knowledge learned in a deep learning model, in
order to obtain good classification performance on new problems, where only a few labeled …

Small datasets are ubiquitous in drug discovery as data generation is expensive and can be
restricted for ethical reasons (eg in vivo experiments). A widely applied technique in early …

Practical object detection application can lose its effectiveness on image inputs with natural
distribution shifts. This problem leads the research community to pay more attention on the …

Few-shot classification is a challenging problem that aims to learn a model that can adapt to
unseen classes given a few labeled samples. Recent approaches pre-train a feature …

Few-shot learning amounts to learning representations and acquiring knowledge such that
novel tasks may be solved with both supervision and data being limited. Improved …

Most current few-shot learning methods train a model from abundantly labeled base
category data and then transfer and adapt the model to sparsely labeled novel category …

Few-shot learning aims to train a model with a limited number of base class samples to
classify the novel class samples. However, to attain generalization with a limited number of …