论文标题
团队PKU-WICT-MIPL PIC化妆时间视频接地挑战2022技术报告
Team PKU-WICT-MIPL PIC Makeup Temporal Video Grounding Challenge 2022 Technical Report
论文作者
论文摘要
在这份技术报告中，我们简要介绍了团队“ PKU-WICT-MIPL”的解决方案，以在ACM-MM 2022中的PIC化妆时间接地（MTVG）挑战（MTVG）挑战。鉴于鉴于未修饰的化妆视频和一个步骤查询，MTVG旨在将目标化妆的时间置于视频中。为了解决这项任务，我们提出了一个短语关系挖掘框架，以利用与细粒度短语和整个句子相关的时间定位关系。此外，我们建议限制不同步骤句子查询的本地化结果，以免通过动态编程算法相互重叠。实验结果证明了我们方法的有效性。我们的最终提交在排行榜上排名第二，从第一个方面只有0.55 \％的差距。
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.
