Few-shot image classification is a challenging problem that aims to achieve the human level
of recognition based only on a small number of training images. One main solution to few …

Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar
objects from different sub-categories with limited supervision. However, traditional few-shot …

Recent graph neural network (GNN) based methods for few-shot learning (FSL) represent
the samples of interest as a fully-connected graph and conduct reasoning on the nodes …

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories
with the help of limited available samples. Undoubtedly, this task inherits the main …

With the emergence of large pretrained vison-language models such as CLIP, transferable
representations can be adapted to a wide range of downstream tasks via prompt tuning …

The goal of few-shot image recognition is to distinguish different categories with only one or
a few training samples. Previous works of few-shot learning mainly work on general object …

Few-shot learning is a tough topic to solve since obtaining a large number of training
samples in real applications is challenging. It has attracted increasing attention recently …

Few-shot learning for visual recognition aims to adapt to novel unseen classes with only a
few images. Recent work, especially the work based on low-level information, has achieved …

Few-shot learning is an essential and challenging field in machine learning since the agent
needs to learn novel concepts with a few data. Recent methods aim to learn comparison or …

We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or
unsupervised few-shot image classification and few-shot action recognition. We leverage so …