  Video-Text Retrieval (VTR) aims to search for the most relevant video related
to the semantics in a given sentence, and vice versa. In general, this
retrieval task is composed of four successive steps: video and textual feature
representation extraction, feature embedding and matching, and objective
functions. In the last, a list of samples retrieved from the dataset is ranked
based on their matching similarities to the query. In recent years, significant
and flourishing progress has been achieved by deep learning techniques,
however, VTR is still a challenging task due to the problems like how to learn
an efficient spatial-temporal video feature and how to narrow the cross-modal
gap. In this survey, we review and summarize over 100 research papers related
to VTR, demonstrate state-of-the-art performance on several commonly
benchmarked datasets, and discuss potential challenges and directions, with the
expectation to provide some insights for researchers in the field of video-text
retrieval.
