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        <title>Span-based Localizing Network for Natural Language Video Localization</title>
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        <namePart type="given">Aixin</namePart>
        <namePart type="family">Sun</namePart>
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    <name type="personal">
        <namePart type="given">Wei</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework, to address NLVL. The proposed VSLNet tackles the differences between NLVL and span-based QA through a simple and yet effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to search for matching video span within a highlighted region. Through extensive experiments on three benchmark datasets, we show that the proposed VSLNet outperforms the state-of-the-art methods; and adopting span-based QA framework is a promising direction to solve NLVL.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.585</identifier>
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%0 Conference Proceedings
%T Span-based Localizing Network for Natural Language Video Localization
%A Zhang, Hao
%A Sun, Aixin
%A Jing, Wei
%A Zhou, Joey Tianyi
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-span
%X Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework, to address NLVL. The proposed VSLNet tackles the differences between NLVL and span-based QA through a simple and yet effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to search for matching video span within a highlighted region. Through extensive experiments on three benchmark datasets, we show that the proposed VSLNet outperforms the state-of-the-art methods; and adopting span-based QA framework is a promising direction to solve NLVL.
%R 10.18653/v1/2020.acl-main.585
%U https://aclanthology.org/2020.acl-main.585
%U https://doi.org/10.18653/v1/2020.acl-main.585
%P 6543-6554

Markdown (Informal)

[Span-based Localizing Network for Natural Language Video Localization](https://aclanthology.org/2020.acl-main.585) (Zhang et al., ACL 2020)

• Span-based Localizing Network for Natural Language Video Localization (Zhang et al., ACL 2020)

ACL

• Hao Zhang, Aixin Sun, Wei Jing, and Joey Tianyi Zhou. 2020. Span-based Localizing Network for Natural Language Video Localization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6543–6554, Online. Association for Computational Linguistics.