Transductive inference has been widely investigated in few-shot image classification but
completely overlooked in the recent fast growing literature on adapting vision-langage …

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn
a rich representation of the input. However, for adapting to downstream tasks, they require a …

Few-shot action recognition aims to recognize new unseen categories with only a few
labeled samples of each class. However, it still suffers from the limitation of inadequate data …

With the goal to detect both the object categories appearing in the training phase and those
never have been observed before testing, zero-shot object detection (ZSD) becomes a …

Few-shot learning (FSL) poses a significant challenge in classifying unseen classes with
limited samples, primarily stemming from the scarcity of data. Although numerous generative …

Existing few-shot learning methods generally focus on designing exquisite structures of
meta-learners for learning task-specific prior to improve the discriminative ability of global …

Few-shot learning (FSL) poses a considerable challenge since it aims to improve the model
generalization ability with limited labeled data. Previous works usually attempt to construct …

Few-shot fine-grained visual classification aims to identify fine-grained concepts with very
few samples, which is widely used in many fields, such as the classification of different …

Few-shot object detection is a challenging task aimed at recognizing novel classes and
localizing with limited labeled data. Although substantial achievements have been obtained …

Few-shot learning (FSL) requires vision models to quickly adapt to brand-new classification
tasks with changing task distributions in the presence of limited annotated samples …