This paper addresses the problem of temporal sentence grounding (TSG), which
aims to identify the temporal boundary of a specific segment from an untrimmed
video by a sentence query. Previous works either compare pre-defined candidate
segments with the query and select the best one by ranking, or directly regress
the boundary timestamps of the target segment. In this paper, we propose a
novel localization framework that scores all pairs of start and end indices
within the video simultaneously with a biaffine mechanism. In particular, we
present a Context-aware Biaffine Localizing Network (CBLN) which incorporates
both local and global contexts into features of each start/end position for
biaffine-based localization. The local contexts from the adjacent frames help
distinguish the visually similar appearance, and the global contexts from the
entire video contribute to reasoning the temporal relation. Besides, we also
develop a multi-modal self-attention module to provide fine-grained
query-guided video representation for this biaffine strategy. Extensive
experiments show that our CBLN significantly outperforms state-of-the-arts on
three public datasets (ActivityNet Captions, TACoS, and Charades-STA),
demonstrating the effectiveness of the proposed localization framework.

本文提出一种新颖的本地化框架，使用双仿射机制以同时为视频中的所有开始和结束索引打分，该框架结合了全局和本地上下文以实现时间句子定位，同时引入多模态自注意机制提供细粒度的查询引导视频表征。大量实验证明，我们的 CBLN 在三个公共数据集上显著优于现有技术，证明了提出的本地化框架的有效性。