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Visual Question Generation: The State of the Art

Authors:

Charulata Patil,

Manasi PatwardhanAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 3

Article No.: 47, Pages 1 - 22

https://doi.org/10.1145/3383465

Published: 28 May 2020 Publication History

Abstract

Visual question generation (VQG) is an interesting problem that has recently received attention. The task of VQG involves generating meaningful questions based on the input image. It is a multi-modal problem involving image understanding and natural language generation, especially using deep learning methods. VQG can be considered as complementary task of visual question answering. In this article, we review the current state of VQG in terms of methods to understand the problem, existing datasets to train the VQG model, evaluation metrics, and algorithms to handle the problem. Finally, we discuss the challenges that need to be conquered and the possible future directions for an effective VQG.

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Index Terms

Visual Question Generation: The State of the Art
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
    2. Natural language processing
      1. Natural language generation

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 3

May 2021

787 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3403423

Editor:
Albert Zomaya
University of Sydney, Australia

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Copyright © 2020 ACM.

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Publication History

Published: 28 May 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 February 2020

Received: 01 November 2019

Published in CSUR Volume 53, Issue 3

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Cited By

Ricci RBazi YMelgani F(2024)Machine-to-Machine Visual Dialoguing with ChatGPT for Enriched Textual Image DescriptionRemote Sensing10.3390/rs1603044116:3(441)Online publication date: 23-Jan-2024
https://doi.org/10.3390/rs16030441
Sychev O(2024)Searching Questions and Learning Problems in Large Problem Banks: Constructing Tests and Assignments on the FlyComputers10.3390/computers1306014413:6(144)Online publication date: 5-Jun-2024
https://doi.org/10.3390/computers13060144
Zheng WYan LWang F(2024)So Many Heads, So Many Wits: Multimodal Graph Reasoning for Text-Based Visual Question AnsweringIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.331996454:2(854-865)Online publication date: Feb-2024
https://doi.org/10.1109/TSMC.2023.3319964
Padaria APatel DGupta RKumar Jadav NTanwar SGarg D(2024)Visual Question Generation Framework for Chess Game State Identification2024 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC460750.2024.10649325(1-6)Online publication date: 15-Mar-2024
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Franciscatto MErpen de Bona LTrois CDidonet Del FabroFabro MDamasceno Lima J(2024)Situational Data Integration in Question Answering systems: a survey over two decadesKnowledge and Information Systems10.1007/s10115-024-02136-066:10(5875-5918)Online publication date: 1-Oct-2024
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Al-Hadhrami SMenai MAl-Ahmadi SAlnafessah A(2023)A Critical Analysis of Benchmarks, Techniques, and Models in Medical Visual Question AnsweringIEEE Access10.1109/ACCESS.2023.333521611(136507-136540)Online publication date: 2023
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Mulla NGharpure P(2023)Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applicationsProgress in Artificial Intelligence10.1007/s13748-023-00295-912:1(1-32)Online publication date: 1-Mar-2023
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Bilal IWang BTsakalidis ANguyen DProcter RLiakata M(2022)Template-based Abstractive Microblog Opinion SummarizationTransactions of the Association for Computational Linguistics10.1162/tacl_a_0051610(1229-1248)Online publication date: 22-Nov-2022
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Patil CAbhyankar A(2022)Effective Generation of Visual Questions2022 International Conference on Signal and Information Processing (IConSIP)10.1109/ICoNSIP49665.2022.10007524(1-5)Online publication date: 26-Aug-2022
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