  The task of temporally grounding language queries in videos is to temporally
localize the best matched video segment corresponding to a given language
(sentence). It requires certain models to simultaneously perform visual and
linguistic understandings. Previous work predominantly ignores the precision of
segment localization. Sliding window based methods use predefined search window
sizes, which suffer from redundant computation, while existing anchor-based
approaches fail to yield precise localization. We address this issue by
proposing an end-to-end boundary-aware model, which uses a lightweight branch
to predict semantic boundaries corresponding to the given linguistic
information. To better detect semantic boundaries, we propose to aggregate
contextual information by explicitly modeling the relationship between the
current element and its neighbors. The most confident segments are subsequently
selected based on both anchor and boundary predictions at the testing stage.
The proposed model, dubbed Contextual Boundary-aware Prediction (CBP),
outperforms its competitors with a clear margin on three public datasets. All
codes are available on https://github.com/JaywongWang/CBP .
