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Given an untrimmed video and a text query, natural language videolocalization (NLVL) is to locate a matching span from the video thatsemantically corresponds to the query. Existing solutions formulate NLVL eitheras a ranking task and apply multimodal matching architecture, or as aregression task to directly regress the target video span. In this work, weaddress NLVL task with a span-based QA approach by treating the input video astext passage. We propose a video span localizing network (VSLNet), on top ofthe standard span-based QA framework, to address NLVL. The proposed VSLNettackles the differences between NLVL and span-based QA through a simple yeteffective query-guided highlighting (QGH) strategy. The QGH guides VSLNet tosearch for matching video span within a highlighted region. Through extensiveexperiments on three benchmark datasets, we show that the proposed VSLNetoutperforms the state-of-the-art methods; and adopting span-based QA frameworkis a promising direction to solve NLVL.

Span-based Localizing Network for Natural Language Video Localization