Meta-learning has gained wide popularity as a training framework that is more data-efficient
than traditional machine learning methods. However, its generalization ability in complex …

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 …

We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint
Temporal and Camera Viewpoint Alignment. To factor out misalignment between query and …

Few-shot learning (FSL) is promising in the field of medical image analysis due to high cost
of establishing high-quality medical datasets. Many FSL approaches have been proposed in …

At present, image classification covers more and more fields, and it is often difficult to obtain
enough data for learning in some specific scenarios, such as medical fields, personalized …

Few-shot learning addresses the challenge of learning how to address novel tasks given not
just limited supervision but limited data as well. An attractive solution is synthetic data …

Despite the growing demand for tuning foundation vision transformers (FViTs) on
downstream tasks, fully unleashing FViTs' potential under data-limited scenarios (eg, few …

Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of
images (eg, birds, flowers, and airplanes) belonging to different subclasses of the same …

Few-shot classification is a challenging task of computer vision and is critical to the data-
sparse scenario like rare disease diagnosis. Feature augmentation is a straightforward way …

Video sequences exhibit significant nuisance variations (undesired effects) of speed of
actions, temporal locations, and subjects' poses, leading to temporal-viewpoint …