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        <title>MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction</title>
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    <abstract>Given a text 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 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.</abstract>
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%0 Conference Proceedings
%T MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction
%A Jing, Wang
%A Sun, Aixin
%A Zhang, Hao
%A Li, Xiaoli
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F jing-etal-2023-ms
%X Given a text 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 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.
%R 10.18653/v1/2023.acl-long.77
%U https://aclanthology.org/2023.acl-long.77
%U https://doi.org/10.18653/v1/2023.acl-long.77
%P 1387-1400

Markdown (Informal)

[MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction](https://aclanthology.org/2023.acl-long.77) (Jing et al., ACL 2023)

• MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction (Jing et al., ACL 2023)

ACL

• Wang Jing, Aixin Sun, Hao Zhang, and Xiaoli Li. 2023. MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1387–1400, Toronto, Canada. Association for Computational Linguistics.