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

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 understanding 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 modalities, we design a novel Video-Language Graph Matching Network (VLG-Net) to match video and query graphs. Core ingredients include representation graphs built atop video snippets and query tokens separately and used to model 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 language queries: ActivityNet-Captions, TACoS, and DiDeMo.

Citation
Soldan, M., Xu, M., Qu, S., Tegner, J., & Ghanem, B. (2021). VLG-Net: Video-Language Graph Matching Network for Video Grounding. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). doi:10.1109/iccvw54120.2021.00361

Acknowledgements
This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the Visual Computing Center (VCC) funding.

Publisher
IEEE

Conference/Event Name
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

DOI
10.1109/ICCVW54120.2021.00361

arXiv
2011.10132

Additional Links
https://ieeexplore.ieee.org/document/9607537/https://ieeexplore.ieee.org/document/9607537/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9607537

Permanent link to this record

Version History

Now showing 1 - 2 of 2
VersionDateSummary
2*
2021-12-19 06:30:49
Published Conference Paper
2020-11-24 13:35:25
* Selected version