  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.
