Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …

Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

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 …

The world provides us with data of multiple modalities. Intuitively, models fusing data from
different modalities outperform their uni-modal counterparts, since more information is …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

Due to the modality gap between visible and infrared images with high visual ambiguity,
learning diverse modality-shared semantic concepts for visible-infrared person re …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

We introduce a conceptually simple yet effective model for self-supervised representation
learning with graph data. It follows the previous methods that generate two views of an input …

Abstract Domain Generalization (DG) aims to train a model, from multiple observed source
domains, in order to perform well on unseen target domains. To obtain the generalization …

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …