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 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 …

With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval
methods struggle to meet the needs of users demanding access to data from various …

Recently, image-text matching has attracted more and more attention from academia and
industry, which is fundamental to understanding the latent correspondence across visual …

Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal
community which aims to retrieve the target person based on a textual query. Although …

Owing to its query and storage efficiency, hash learning has sparked much interest for Cross-
Modal Retrieval (CMR) task. Previous literatures have proved the superiority of supervised …

Despite the success of multimodal learning in cross-modal retrieval task, the remarkable
progress relies on the correct correspondence among multimedia data. However, collecting …

With the growing amount of multimodal data, cross-modal retrieval has attracted more and
more attention and become a hot research topic. To date, most of the existing techniques …

Deep cross-modal hashing has become an essential tool for supervised multimodal search.
These models tend to be optimized with large, curated multimodal datasets, where most …