Video-Text Retrieval has been a hot research topic with the explosion of multimedia data on the Internet. Transformer for video-text learning has attracted increasing attention due to the promising performance.However, existing cross-modal transformer approaches typically suffer from two major limitations: 1) Limited exploitation of the transformer architecture where different layers have different feature characteristics. 2) End-to-end training mechanism limits negative interactions among samples in a mini-batch. In this paper, we propose a novel approach named Hierarchical Transformer (HiT) for video-text retrieval. HiT performs hierarchical cross-modal contrastive matching in feature-level and semantic-level to achieve multi-view and comprehensive retrieval results. Moreover, inspired by MoCo, we propose Momentum Cross-modal Contrast for cross-modal learning to enable large-scale negative interactions on-the-fly, which contributes to the generation of more precise and discriminative representations. Experimental results on three major Video-Text Retrieval benchmark datasets demonstrate the advantages of our methods.

提出了一种新的名为Hierarchical Transformer (HiT)的方法，其中包括Hierarchical Cross-modal Contrastive Matching和Momentum Cross-modal Contrast的元素，以解决现有交叉模式Transformer方法存在的局限性，并在三个主要的Video-Text检索基准数据集上实现了优越的实验结果。

HiT：层次化动量对比视频-文本检索的Transformer模型