Deep adversarial quantization network for cross-modal retrieval

Yu ZHOU, Yong FENG*, Mingliang ZHOU*, Baohua QIANG, Leong Hou U, Jiajie ZHU

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

3 Citations (Scopus)

Abstract

In this paper, we propose a seamless multimodal binary learning method for cross-modal retrieval. First, we utilize adversarial learning to learn modality-independent representations of different modalities. Second, we formulate loss function through the Bayesian approach, which aims to jointly maximize correlations of modality-independent representations and learn the common quantizer codebooks for both modalities. Based on the common quantizer codebooks, our method performs efficient and effective cross-modal retrieval with fast distance table lookup. Extensive experiments on three cross-modal datasets demonstrate that our method outperforms state-of-the-art methods. The source code is available at https://github.com/zhouyu1996/DAQN.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings
PublisherIEEE
Pages4325-4329
Number of pages5
Volume2021-June
ISBN (Electronic)9781728176055
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)1520-6149

Conference

Conference2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021
Country/TerritoryCanada
CityVirtual, Toronto
Period6/06/2111/06/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Adversarial learning
  • Cross-modal retrieval
  • Innerproduct similarity
  • Quantization

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