Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

Multi-view learning aims to combine multiple features to achieve more comprehensive
descriptions of data. Most previous works assume that multiple views are strictly aligned …

The success of existing multi-view clustering methods heavily relies on the assumption of
view consistency and instance completeness, referred to as the complete information …

In this paper, we study two challenging problems in incomplete multi-view clustering
analysis, namely, i) how to learn an informative and consistent representation among …

In this paper, we study an untouched problem in visible-infrared person re-identification (VI-
ReID), namely, Twin Noise Labels (TNL) which refers to as noisy annotation and …

Cross-modal matching, which aims to establish the correspondence between two different
modalities, is fundamental to a variety of tasks such as cross-modal retrieval and vision-and …

Recently, federated multi-view clustering (FedMVC) has emerged as a powerful tool to
uncover complementary cluster structures across distributed clients, gaining significant …