  Temporal video grounding (TVG) aims to localize a target segment in a video
according to a given sentence query. Though respectable works have made decent
achievements in this task, they severely rely on abundant video-query paired
data, which is expensive and time-consuming to collect in real-world scenarios.
In this paper, we explore whether a video grounding model can be learned
without any paired annotations. To the best of our knowledge, this paper is the
first work trying to address TVG in an unsupervised setting. Considering there
is no paired supervision, we propose a novel Deep Semantic Clustering Network
(DSCNet) to leverage all semantic information from the whole query set to
compose the possible activity in each video for grounding. Specifically, we
first develop a language semantic mining module, which extracts implicit
semantic features from the whole query set. Then, these language semantic
features serve as the guidance to compose the activity in video via a
video-based semantic aggregation module. Finally, we utilize a foreground
attention branch to filter out the redundant background activities and refine
the grounding results. To validate the effectiveness of our DSCNet, we conduct
experiments on both ActivityNet Captions and Charades-STA datasets. The results
demonstrate that DSCNet achieves competitive performance, and even outperforms
most weakly-supervised approaches.
