Finding relevant moments and highlights in videos according to natural
language queries is a natural and highly valuable common need in the current
video content explosion era. Nevertheless, jointly conducting moment retrieval
and highlight detection is an emerging research topic, even though its
component problems and some related tasks have already been studied for a
while. In this paper, we present the first unified framework, named Unified
Multi-modal Transformers (UMT), capable of realizing such joint optimization
while can also be easily degenerated for solving individual problems. As far as
we are aware, this is the first scheme to integrate multi-modal (visual-audio)
learning for either joint optimization or the individual moment retrieval task,
and tackles moment retrieval as a keypoint detection problem using a novel
query generator and query decoder. Extensive comparisons with existing methods
and ablation studies on QVHighlights, Charades-STA, YouTube Highlights, and
TVSum datasets demonstrate the effectiveness, superiority, and flexibility of
the proposed method under various settings. Source code and pre-trained models
are available at this https URL

本研究提出了一个名为 UMT 的统一多模态转换器的框架，能够实现时刻检索和精华视频检测的联合优化，并且能够轻松地退化为解决单个问题。实验结果表明，该方法在各种设置下都具有有效性、优越性和灵活性。