mlsanityData to 14 Oct 2026

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

arXiv 2305.18969 ↗First posted 30 May 2023cs.CVcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 34 of its 49 references that are in the map. First posted on arXiv 30 May 2023.

Builds on

34 of 49 references.

References · 34

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22 more
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Citing papers per year: 2023: 1; 2024: 3; 2025: 4.

8 papers.