Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

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 …

Image-text matching plays a critical role in bridging the vision and language, and great
progress has been made by exploiting the global alignment between image and sentence …

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 retrieval methods build a common representation space for samples from
multiple modalities, typically from the vision and the language domains. For images and …

Change detection is an important tool for long-term Earth observation missions. It takes bi-
temporal images as input and predicts “where” the change has occurred. Different from other …

Image-text retrieval is a fundamental and crucial branch in information retrieval. Although
much progress has been made in bridging vision and language, it remains challenging …

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …

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 …

Self-supervised monocular depth estimation has been widely studied, owing to its practical
importance and recent promising improvements. However, most works suffer from limited …