This paper addresses the problem of text-to-video temporal grounding, which
aims to identify the time interval in a video semantically relevant to a text
query. We tackle this problem using a novel regression-based model that learns
to extract a collection of mid-level features for semantic phrases in a text
query, which corresponds to important semantic entities described in the query
(e.g., actors, objects, and actions), and reflect bi-modal interactions between
the linguistic features of the query and the visual features of the video in
multiple levels. The proposed method effectively predicts the target time
interval by exploiting contextual information from local to global during
bi-modal interactions. Through in-depth ablation studies, we find out that
incorporating both local and global context in video and text interactions is
crucial to the accurate grounding. Our experiment shows that the proposed
method outperforms the state of the arts on Charades-STA and ActivityNet
Captions datasets by large margins, 7.44\% and 4.61\% points at Recall@tIoU=0.5
metric, respectively. Code is available in
this https URL

该论文提出了一种基于回归模型的方法，使用文本查询中的语义短语提取中间特征，以反映查询中描述的重要语义实体和视频的视觉特征之间的双模态交互，通过在多个层面上从局部到全局利用上下文信息，有效地预测目标时间区间。实验证明，该方法在 Charades-STA 和 ActivityNet Captions 数据集上的表现明显优于现有方法。