research-article

Inner Knowledge-based Img2Doc Scheme for Visual Question Answering

Authors:

Richang HongAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 3

Article No.: 76, Pages 1 - 21

https://doi.org/10.1145/3489142

Published: 04 March 2022 Publication History

Abstract

Visual Question Answering (VQA) is a research topic of significant interest at the intersection of computer vision and natural language understanding. Recent research indicates that attributes and knowledge can effectively improve performance for both image captioning and VQA. In this article, an inner knowledge-based Img2Doc algorithm for VQA is presented. The inner knowledge is characterized as the inner attribute relationship in visual images. In addition to using an attribute network for inner knowledge-based image representation, VQA scheme is associated with a question-guided Doc2Vec method for question–answering. The attribute network generates inner knowledge-based features for visual images, while a novel question-guided Doc2Vec method aims at converting natural language text to vector features. After the vector features are extracted, they are combined with visual image features into a classifier to provide an answer. Based on our model, the VQA problem is resolved by textual question answering. The experimental results demonstrate that the proposed method achieves superior performance on multiple benchmark datasets.

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Index Terms

Inner Knowledge-based Img2Doc Scheme for Visual Question Answering
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual content-based indexing and retrieval
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction
      2. Question answering

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 3

August 2022

478 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3505208

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Publication History

Published: 04 March 2022

Accepted: 01 September 2021

Received: 01 May 2021

Published in TOMM Volume 18, Issue 3

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National Natural Science Foundation of China
Nature Science Foundation of Jiangsu for Distinguished Young Scientist
Postdoctoral Research Plan of Jiangsu Province
Postdoctoral Science Foundation of China
Nanjing University of Posts and Telecommunications Program

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Han TZhou QYu JYu ZZhang JZhao S(2024)Effective Video Summarization by Extracting Parameter-Free Motion AttentionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365467020:7(1-20)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3654670
He WLi ZWang HXu TWang ZHuai BYuan NChen E(2024)Multimodal Dialogue Systems via Capturing Context-aware Dependencies and Ordinal Information of Semantic ElementsACM Transactions on Intelligent Systems and Technology10.1145/364509915:3(1-25)Online publication date: 15-Apr-2024
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