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    VLG-Net: Video-Language Graph Matching Network for Video Grounding

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    Preprintfile1.pdf
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    Type
    Preprint
    Authors
    Qu, Sisi
    Soldan, Mattia
    Xu, Mengmeng cc
    Tegner, Jesper cc
    Ghanem, Bernard cc
    KAUST Department
    Bioengineering Program
    Biological and Environmental Sciences and Engineering (BESE) Division
    Electrical Engineering
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Bioscience Program
    Date
    2020-11-19
    Permanent link to this record
    http://hdl.handle.net/10754/666102
    
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    Abstract
    Grounding language queries in videos aims at identifying the time interval (or moment) semantically relevant to a language query. The solution to this challenging task demands the understanding of videos' and queries' semantic content and the fine-grained reasoning about their multi-modal interactions. Our key idea is to recast this challenge into an algorithmic graph matching problem. Fueled by recent advances in Graph Neural Networks, we propose to leverage Graph Convolutional Networks to model video and textual information as well as their semantic alignment. To enable the mutual exchange of information across the domains, we design a novel Video-Language Graph Matching Network (VLG-Net) to match video and query graphs. Core ingredients include representation graphs, built on top of video snippets and query tokens separately, which are used for modeling the intra-modality relationships. A Graph Matching layer is adopted for cross-modal context modeling and multi-modal fusion. Finally, moment candidates are created using masked moment attention pooling by fusing the moment's enriched snippet features. We demonstrate superior performance over state-of-the-art grounding methods on three widely used datasets for temporal localization of moments in videos with natural language queries: ActivityNet-Captions, TACoS, and DiDeMo.
    Publisher
    arXiv
    arXiv
    2011.10132
    Additional Links
    https://arxiv.org/pdf/2011.10132
    Collections
    Bioengineering Program; Biological and Environmental Sciences and Engineering (BESE) Division; Preprints; Bioscience Program; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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