  Given a query, the task of Natural Language Video Localization (NLVL) is to
localize a temporal moment in an untrimmed video that semantically matches the
query. In this paper, we adopt a proposal-based solution that generates
proposals (i.e., candidate moments) and then select the best matching proposal.
On top of modeling the cross-modal interaction between candidate moments and
the query, our proposed Moment Sampling DETR (MS-DETR) enables efficient
moment-moment relation modeling. The core idea is to sample a subset of moments
guided by the learnable templates with an adopted DETR (DEtection TRansformer)
framework. To achieve this, we design a multi-scale visual-linguistic encoder,
and an anchor-guided moment decoder paired with a set of learnable templates.
Experimental results on three public datasets demonstrate the superior
performance of MS-DETR.
