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Given a query, the task of Natural Language Video Localization (NLVL) is tolocalize a temporal moment in an untrimmed video that semantically matches thequery. In this paper, we adopt a proposal-based solution that generatesproposals (i.e., candidate moments) and then select the best matching proposal.On top of modeling the cross-modal interaction between candidate moments andthe query, our proposed Moment Sampling DETR (MS-DETR) enables efficientmoment-moment relation modeling. The core idea is to sample a subset of momentsguided 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 superiorperformance of MS-DETR.

MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction