This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

In the past few years, cross-modal image-text retrieval (ITR) has experienced increased
interest in the research community due to its excellent research value and broad real-world …

In recent years, the growing number of medical imaging studies is placing an ever-
increasing burden on radiologists. Deep learning provides a promising solution for …

Image-text matching, as a fundamental task, bridges the gap between vision and language.
The key of this task is to accurately measure similarity between these two modalities. Prior …

Learning medical visual representations directly from paired radiology reports has become
an emerging topic in representation learning. However, existing medical image-text joint …

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of
human. Despite tremendous success in the AI research, most of existing methods have only …

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 …

Current state-of-the-art image-text matching methods implicitly align the visual-semantic
fragments, like regions in images and words in sentences, and adopt cross-attention …

Image-text matching, a bridge connecting image and language, is an important task, which
generally learns a holistic cross-modal embedding to achieve a high-quality semantic …

Visual appearance is considered to be the most important cue to understand images for
cross-modal retrieval, while sometimes the scene text appearing in images can provide …