  Given an untrimmed video and a text query, natural language video
localization (NLVL) is to locate a matching span from the video that
semantically corresponds to the query. Existing solutions formulate NLVL either
as a ranking task and apply multimodal matching architecture, or as a
regression task to directly regress the target video span. In this work, we
address NLVL task with a span-based QA approach by treating the input video as
text passage. We propose a video span localizing network (VSLNet), on top of
the standard span-based QA framework, to address NLVL. The proposed VSLNet
tackles the differences between NLVL and span-based QA through a simple yet
effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to
search for matching video span within a highlighted region. Through extensive
experiments on three benchmark datasets, we show that the proposed VSLNet
outperforms the state-of-the-art methods; and adopting span-based QA framework
is a promising direction to solve NLVL.
