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 …

Cross-modal retrieval has become an active study field with the expanding scale of
multimodal data. To date, most existing methods transform multimodal data into a common …

The cross-modal retrieval task has raised continuous attention in recent years with the
increasing scale of multi-modal data, which has broad application prospects including …

The main challenge of cross-modal retrieval is to learn the consistent embedding for
heterogeneous modalities. To solve this problem, traditional labelwise cross-modal …

Cross-modal retrieval aims to realize accurate and flexible retrieval across different
modalities of data, eg, image and text, which has achieved significant progress in recent …

Accurately matching visual and textual data in cross-modal retrieval has been widely studied
in the multimedia community. To address these challenges posited by the heterogeneity gap …

Abstract Information retrieval among different modalities becomes a significant issue with
many promising applications. However, inconsistent feature representation of various …

How to eliminate the semantic gap between multi-modal data and effectively fuse multi-
modal data is the key problem of cross-modal retrieval. The abstractness of semantics …

Recently, cross-modal retrieval is emerging with the help of deep multimodal learning.
However, even for unimodal data, collecting large-scale well-annotated data is expensive …

Cross-modal retrieval aims at retrieving relevant points across different modalities, such as
retrieving images via texts. One key challenge of cross-modal retrieval is narrowing the …