The task of language-guided video temporal grounding is to localize the
particular video clip corresponding to a query sentence in an untrimmed video.
Though progress has been made continuously in this field, some issues still
need to be resolved. First, most of the existing methods rely on the
combination of multiple complicated modules to solve the task. Second, due to
the semantic gaps between the two different modalities, aligning the
information at different granularities (local and global) between the video and
the language is significant, which is less addressed. Last, previous works do
not consider the inevitable annotation bias due to the ambiguities of action
boundaries. To address these limitations, we propose a simple two-branch
Cross-Modality Attention (CMA) module with intuitive structure design, which
alternatively modulates two modalities for better matching the information both
locally and globally. In addition, we introduce a new task-specific regression
loss function, which improves the temporal grounding accuracy by alleviating
the impact of annotation bias. We conduct extensive experiments to validate our
method, and the results show that just with this simple model, it can
outperform the state of the arts on both Charades-STA and ActivityNet Captions
datasets.

本文主要研究了利用语言引导定位视频中的相关片段的问题，提出一种简单直观的跨模态注意力模块 (CMA) 和针对此任务的新回归损失函数来提高定位精度，并在 Charades-STA 和 ActivityNet Captions 数据集上超越了目前最先进的方法。