Semantics Consistent Adversarial Cross-Modal Retrieval

  • Ruisheng Xuan
  • Weihua OuEmail author
  • Quan Zhou
  • Yongfeng Cao
  • Hua Yang
  • Xiangguang Xiong
  • Fangming Ruan
Part of the Studies in Computational Intelligence book series (SCI, volume 810)


Cross-modal retrieval returns the relevant results from the other modalities given a query from one modality. The main challenge of cross-modal retrieval is the “heterogeneity gap” amongst modalities, because different modalities have different distributions and representations. Therefore, the similarity of different modalities can not be measured directly. In this paper, we propose a semantics consistent adversarial cross-modal retrieval approach, which learns a semantics consistent representation for different modalities with same semantic category. Specifically, we encourage the class center of different modalities with same semantic label to be as close as possible, and also minimize the distances between the samples and the class center with same semantic label from different modalities. Comprehensive experiments on Wikipedia dataset are conducted and the experimental results show the efficiency and effectiveness of our approach in cross-modal retrieval.


Cross-modal retrieval Adversarial learning Semantics consistent Common subspace Media gap 



Weihua Ou is the corresponding author of this paper. This work was partly supported by the National Natural Science Foundation of China (Grant No. 61762021, 61876093, 61402122, 61881240048), Natural Science Foundation of Guizhou Province (Grant No. [2017]1130), the 2014 Ph.D. Recruitment Program of Guizhou Normal University, Foundation of Guizhou Educational Department (KY[2016]027), HIRP Open 2018 Project of Huawei, the Natural Science Foundation of Educational Commission of Guizhou Province under Grant No. [2015]434, Guizhou Province Innovation Talents Team of Electrostatic and Electromagnetic Protection (No. QKHPTRC[2017]5653), Key Subjects Construction of Guizhou Province (ZDXK[2016]8).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ruisheng Xuan
    • 1
  • Weihua Ou
    • 1
    Email author
  • Quan Zhou
    • 2
    • 3
  • Yongfeng Cao
    • 1
  • Hua Yang
    • 1
  • Xiangguang Xiong
    • 1
  • Fangming Ruan
    • 1
  1. 1.School of Big Data and Computer ScienceGuizhou Normal UniversityGuiyangPeople’s Republic of China
  2. 2.National Engineering Research Center of Communications and NetworkingNanjing University of Posts & TelecommunicationsNanjingPeople’s Republic of China
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China

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