HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval

• URL: http://arxiv.org/abs/2103.15049v1
• Date: Sun, 28 Mar 2021 04:52:25 GMT
• Title: HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval
• Authors: Song Liu and Haoqi Fan and Shengsheng Qian and Yiru Chen and Wenkui Ding and Zhongyuan Wang
• Abstract summary: 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. Inspired by MoCo, we propose Momentum Cross-modal Contrast for cross-modal learning to enable large-scale negative interactions on-the-fly.
• Score: 40.646628490887075
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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