We present a framework for learning cross-modal video representations by
directly pre-training on raw data to facilitate various downstream video-text
tasks. Our main contributions lie in the pre-training framework and proxy
tasks. First, based on the shortcomings of two mainstream pixel-level
pre-training architectures (limited applications or less efficient), we propose
Shared Network Pre-training (SNP). By employing one shared BERT-type network to
refine textual and cross-modal features simultaneously, SNP is lightweight and
could support various downstream applications. Second, based on the intuition
that people always pay attention to several "significant words" when
understanding a sentence, we propose the Significant Semantic Strengthening
(S3) strategy, which includes a novel masking and matching proxy task to
promote the pre-training performance. Experiments conducted on three downstream
video-text tasks and six datasets demonstrate that, we establish a new
state-of-the-art in pixel-level video-text pre-training; we also achieve a
satisfactory balance between the pre-training efficiency and the fine-tuning
performance. The codebase are available at
this https URL

我们提出了一种通过直接在原始数据上进行预训练以促进各种下游视频文本任务的跨模态视频表示学习框架，并在预训练框架和代理任务方面做出了主要贡献。