Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HiT: Hierarchical Transformer with Momentum Contrast for Video-Text
  Retrieval   Video-Text Retrieval has been a hot research topic with the growth of
multimedia data on the internet. Transformer for video-text learning has
attracted increasing attention due to its promising performance. However,
existing cross-modal transformer approaches typically suffer from two major
limitations: 1) Exploitation of the transformer architecture where different
layers have different feature characteristics is limited; 2) End-to-end
training mechanism limits negative sample interactions 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 both feature-level and semantic-level, achieving 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 sample interactions on-the-fly, which contributes to the generation of
more precise and discriminative representations. Experimental results on the
three major Video-Text Retrieval benchmark datasets demonstrate the advantages
of our method.

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The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex