  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 application. It is designed for the scenarios where
the queries are from one modality and the retrieval galleries from another
modality. This paper presents a comprehensive and up-to-date survey on the ITR
approaches from four perspectives. By dissecting an ITR system into two
processes: feature extraction and feature alignment, we summarize the recent
advance of the ITR approaches from these two perspectives. On top of this, the
efficiency-focused study on the ITR system is introduced as the third
perspective. To keep pace with the times, we also provide a pioneering overview
of the cross-modal pre-training ITR approaches as the fourth perspective.
Finally, we outline the common benchmark datasets and valuation metric for ITR,
and conduct the accuracy comparison among the representative ITR approaches.
Some critical yet less studied issues are discussed at the end of the paper.
