This technical report presents the 3rd winning solution for MTVG, a new task
introduced in the 4-th Person in Context (PIC) Challenge at ACM MM 2022. MTVG
aims at localizing the temporal boundary of the step in an untrimmed video
based on a textual description. The biggest challenge of this task is the fi
ne-grained video-text semantics of make-up steps. However, current methods
mainly extract video features using action-based pre-trained models. As actions
are more coarse-grained than make-up steps, action-based features are not
sufficient to provide fi ne-grained cues. To address this issue,we propose to
achieve fi ne-grained representation via exploiting feature diversities.
Specifically, we proposed a series of methods from feature extraction, network
optimization, to model ensemble. As a result, we achieved 3rd place in the MTVG
competition.

本技术报告介绍了本文在 ACM MM 2022 的第 4 届 PIC 挑战中所提出的 MTVG 新任务的第 3 名获胜解决方案，该任务旨在基于文本描述确定未修剪视频中步骤的时间边界。我们通过利用特征的多样性，提出了从特征提取、网络优化到模型集成的一系列方法，实现了细粒度表示，并在 MTVG 竞赛中获得了第三名。