Temporal language grounding in videos aims to localize the temporal span
relevant to the given query sentence. Previous methods treat it either as a
boundary regression task or a span extraction task. This paper will formulate
temporal language grounding into video reading comprehension and propose a
Relation-aware Network (RaNet) to address it. This framework aims to select a
video moment choice from the predefined answer set with the aid of
coarse-and-fine choice-query interaction and choice-choice relation
construction. A choice-query interactor is proposed to match the visual and
textual information simultaneously in sentence-moment and token-moment levels,
leading to a coarse-and-fine cross-modal interaction. Moreover, a novel
multi-choice relation constructor is introduced by leveraging graph convolution
to capture the dependencies among video moment choices for the best choice
selection. Extensive experiments on ActivityNet-Captions, TACoS, and
Charades-STA demonstrate the effectiveness of our solution. Codes have been
available.

本文提出了一种使用选择 - 查询交互和选择 - 选择关系构建解决视频阅读理解中的时间语言接地问题的框架，并引入了一种新颖的多选择关系构造器，通过利用图卷积捕获最佳选择的视频时刻之间的依赖关系，并在 ActivityNet-Captions、TACoS 和 Charades-STA 数据集上进行了广泛的实验证明了我们的方法的有效性。