This paper investigates the effective utilization of unlabeled data for large-area cross-view
geo-localization (CVGL) encompassing both unsupervised and semi-supervised settings …

Recently, despite the unprecedented success of large pre-trained visual-language models
(VLMs) on a wide range of downstream tasks, the real-world unsupervised domain …

Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing
GNNs' generalization ability will degrade when there exist distribution shifts between testing …

As for few-shot image classification, recently, some works revisit the standard transfer
learning paradigm, ie, pre-training and fine-tuning, and have achieved some success …

Deep learning has achieved tremendous success in recent years, but most of these
successes are built on an independent and identically distributed (IID) assumption. This …

Estimating the individuals' potential response to varying treatment doses is crucial for
decision-making in areas such as precision medicine and management science. Most …

Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is
troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based …

We introduce a new problem in unsupervised domain adaptation, termed as Generalized
Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target …

In database of recommender systems, users' ratings for most items are usually missing,
resulting in selection bias when users selectively choose items to rate. To address this …

Out-of-distribution (OOD) problems in few-shot classification (FSC) occur when novel
classes sampled from testing distributions differ from base classes drawn from training …