  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.

Computer Vision and Pattern Recognition