  In this technical report, we briefly introduce the solutions of our team
`PKU-WICT-MIPL' for the PIC Makeup Temporal Video Grounding (MTVG) Challenge in
ACM-MM 2022. Given an untrimmed makeup video and a step query, the MTVG aims to
localize a temporal moment of the target makeup step in the video. To tackle
this task, we propose a phrase relationship mining framework to exploit the
temporal localization relationship relevant to the fine-grained phrase and the
whole sentence. Besides, we propose to constrain the localization results of
different step sentence queries to not overlap with each other through a
dynamic programming algorithm. The experimental results demonstrate the
effectiveness of our method. Our final submission ranked 2nd on the
leaderboard, with only a 0.55\% gap from the first.
