  Temporal grounding aims to predict a time interval of a video clip
corresponding to a natural language query input. In this work, we present
EVOQUER, a temporal grounding framework incorporating an existing text-to-video
grounding model and a video-assisted query generation network. Given a query
and an untrimmed video, the temporal grounding model predicts the target
interval, and the predicted video clip is fed into a video translation task by
generating a simplified version of the input query. EVOQUER forms closed-loop
learning by incorporating loss functions from both temporal grounding and query
generation serving as feedback. Our experiments on two widely used datasets,
Charades-STA and ActivityNet, show that EVOQUER achieves promising improvements
by 1.05 and 1.31 at<EMAIL_ADDRESS>We also discuss how the query generation task could
facilitate error analysis by explaining temporal grounding model behavior.
