  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 modalities. To address this, cross-modal retrieval has emerged,
enabling interaction across modalities, facilitating semantic matching, and
leveraging complementarity and consistency between different modal data.
Although prior literature undertook a review of the cross-modal retrieval
field, it exhibits numerous deficiencies pertaining to timeliness, taxonomy,
and comprehensiveness. This paper conducts a comprehensive review of
cross-modal retrieval's evolution, spanning from shallow statistical analysis
techniques to vision-language pre-training models. Commencing with a
comprehensive taxonomy grounded in machine learning paradigms, mechanisms, and
models, the paper then delves deeply into the principles and architectures
underpinning existing cross-modal retrieval methods. Furthermore, it offers an
overview of widely used benchmarks, metrics, and performances. Lastly, the
paper probes the prospects and challenges that confront contemporary
cross-modal retrieval, while engaging in a discourse on potential directions
for further progress in the field. To facilitate the research on cross-modal
retrieval, we develop an open-source code repository at
https://github.com/BMC-SDNU/Cross-Modal-Retrieval.
