With the widespread popularity of user-generated short videos, it becomes
increasingly challenging for content creators to promote their content to
potential viewers. Automatically generating appealing titles and covers for
short videos can help grab viewers' attention. Existing studies on video
captioning mostly focus on generating factual descriptions of actions, which do
not conform to video titles intended for catching viewer attention.
Furthermore, research for cover selection based on multimodal information is
sparse. These problems motivate the need for tailored methods to specifically
support the joint task of short video title generation and cover selection
(TG-CS) as well as the demand for creating corresponding datasets to support
the studies. In this paper, we first collect and present a real-world dataset
named Short Video Title Generation (SVTG) that contains videos with appealing
titles and covers. We then propose a Title generation and Cover selection with
attention Refinement (TCR) method for TG-CS. The refinement procedure
progressively selects high-quality samples and highly relevant frames and text
tokens within each sample to refine model training. Extensive experiments show
that our TCR method is superior to various existing video captioning methods in
generating titles and is able to select better covers for noisy real-world
short videos.

本文介绍了一种名为 Short Video Title Generation (SVTG) 的真实数据集，以及一种应用多模态信息的 Title generation and Cover selection with attention Refinement (TCR) 方法，通过对高质量样本和高相关性帧和文本令牌进行精细化训练，该方法在生成标题和选择封面方面优于现有的各种视频字幕方法，并且可以针对真实短视频选择更好的封面。