  Temporal sentence grounding in videos(TSGV), which aims to localize one
target segment from an untrimmed video with respect to a given sentence query,
has drawn increasing attentions in the research community over the past few
years. Different from the task of temporal action localization, TSGV is more
flexible since it can locate complicated activities via natural languages,
without restrictions from predefined action categories. Meanwhile, TSGV is more
challenging since it requires both textual and visual understanding for
semantic alignment between two modalities(i.e., text and video). In this
survey, we give a comprehensive overview for TSGV, which i) summarizes the
taxonomy of existing methods, ii) provides a detailed description of the
evaluation protocols(i.e., datasets and metrics) to be used in TSGV, and iii)
in-depth discusses potential problems of current benchmarking designs and
research directions for further investigations. To the best of our knowledge,
this is the first systematic survey on temporal sentence grounding. More
specifically, we first discuss existing TSGV approaches by grouping them into
four categories, i.e., two-stage methods, end-to-end methods, reinforcement
learning-based methods, and weakly supervised methods. Then we present the
benchmark datasets and evaluation metrics to assess current research progress.
Finally, we discuss some limitations in TSGV through pointing out potential
problems improperly resolved in the current evaluation protocols, which may
push forwards more cutting edge research in TSGV. Besides, we also share our
insights on several promising directions, including three typical tasks with
new and practical settings based on TSGV.
