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Multimodal Learning with Triplet Ranking Loss for Visual Semantic Embedding Learning

  • Zhanbo Yang
  • Li LiEmail author
  • Jun He
  • Zixi Wei
  • Li Liu
  • Jun Liao
Conference paper
  • 886 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11775)

Abstract

Semantic embedding learning for image and text has been well studied in recent years. In this paper, we present a simple while effective dual-encoder (image encoder and text encoder) framework to unify image and text into a common embedding space. Inspired by deep metric learning, we utilize triplet ranking loss to minimize the gap between the two embedding spaces. We train and test our proposed framework on Flickr8k, Flickr30k and MS-COCO datasets respectively, and evaluate the framework on the Corel1k benchmark dataset as an application. Using VGG-19 for image encoder, GRU for text encoder and triplet ranking loss, we gained obvious improvement versus baseline model on image annotation and image search tasks. Additionally, we explore the vector generated by our image encoder and the one by word embedding of plain word for some arithmetic operations. The above experiments demonstrate the effectiveness of our proposed learning framework.

Keywords

Visual semantic embedding Triplet ranking loss Multimodal learning Word embedding 

Notes

Acknowledgments

The authors would also like to thank the anonymous referees for their valuable comments and helpful suggestions. This work was supported by NSFC (grant No. 61877051) and CSTC (grant No. cstc2018jscx-msyb1042, cstc2017zdcy-zdyf0366 and cstc2017rgzn-zdyf0064).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhanbo Yang
    • 1
  • Li Li
    • 1
    Email author
  • Jun He
    • 1
  • Zixi Wei
    • 1
  • Li Liu
    • 2
  • Jun Liao
    • 2
  1. 1.School of Computer and Information ScienceSouthwest UniversityChongqingPeople’s Republic of China
  2. 2.School of Big Data and Software EngineeringChongqing UniversityChongqingPeople’s Republic of China

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