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A Hierarchical Approach for Visual Storytelling Using Image Description

  • Md. Sultan Al NahianEmail author
  • Tasmia Tasrin
  • Sagar Gandhi
  • Ryan Gaines
  • Brent Harrison
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11869)

Abstract

One of the primary challenges of visual storytelling is developing techniques that can maintain the context of the story over long event sequences to generate human-like stories. In this paper, we propose a hierarchical deep learning architecture based on encoder-decoder networks to address this problem. To better help our network maintain this context while also generating long and diverse sentences, we incorporate natural language image descriptions along with the images themselves to generate each story sentence. We evaluate our system on the Visual Storytelling (VIST) dataset [7] and show that our method outperforms state-of-the-art techniques on a suite of different automatic evaluation metrics. The empirical results from this evaluation demonstrate the necessities of different components of our proposed architecture and shows the effectiveness of the architecture for visual storytelling.

Keywords

Visual storytelling Deep learning Natural language processing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Md. Sultan Al Nahian
    • 1
    Email author
  • Tasmia Tasrin
    • 1
  • Sagar Gandhi
    • 1
  • Ryan Gaines
    • 1
  • Brent Harrison
    • 1
  1. 1.Department of Computer ScienceUniversity of KentuckyLexingtonUSA

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