  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.
