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An analysis of graph convolutional networks and recent datasets for visual question answering

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Abstract

Graph neural network is a deep learning approach widely applied on structural and non-structural scenarios due to its substantial performance and interpretability recently. In a non-structural scenario, textual and visual research topics like visual question answering (VQA) are important, which need graph reasoning models. VQA aims to build a system that can answer related questions about given images as well as understand the underlying semantic meaning behind the image. The critical issues in VQA are to effectively extract the visual and textual features and subject both features into a common space. These issues have a great impact in handling goal-driven, reasoning, and scene classification subtasks. In the same vein, it is difficult to compare models' performance because most existing datasets do not group instances into meaningful categories. With the recent advances in graph-based models, lots of efforts have been devoted to solving the problems mentioned above. This study focuses on graph convolutional networks (GCN) studies and recent datasets for visual question answering tasks. Specifically, we reviewed current related studies on GCN for the VQA task. Also, 18 common and recent datasets for VQA are well studied, though not all of them are discussed at the same level of detail. A critical review of GCN, datasets and VQA challenges is further highlighted. Finally, this study will help researchers to choose a suitable dataset for a particular VQA subtask, identify VQA challenges, the pros and cons of its approaches, and improve more on GCN for the VQA.

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Acknowledgements

We thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by the National Key R&D Program of China No. 2017YFB1002101 and the Joint Advanced Research Foundation of China Electronics Technology Group Corporation No. 6141B08010102.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Abdulganiyu Abdu Yusuf, Feng Chong & Mao Xianling

  2. South-East Information Technology Institute of Beijing Institute of Technology, Beijing, China

    Feng Chong

  3. Beijing Engineering Research Centre of High Volume Language Information Processing and Cloud Computing Application, Beijing, China

    Mao Xianling

  4. National Biotechnology Development Agency, Abuja, Nigeria

    Abdulganiyu Abdu Yusuf

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Correspondence to Abdulganiyu Abdu Yusuf.

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Appendices

Appendix 1

Datasets

Link

VISUAL7W

www.ai.stanford.edu/~yukez/visual7w/

CLEVR

www.cs.stanford.edu/people/jcjohns/clevr

VISUAL GENOME

https://visualgenome.org

TDIUC

www.goo.gl/Ng9ix4

TQA

www.textbookqa.org

VQA 2.0

www.visualqa.org

FIGUREQA

www.datasets.maluuba.com/FigureQA

FVQA

www.metatext.io/datasets/fact-based-visual-question-answering-(fvqa)

VIZWIZ

www.vizwiz.org/tasks-and-datasets/vqa/

DVQA

www.textbookqa.org

VQA-MED

www.imageclef.org/2021/medical/vqa

OCR-VQA

www.ocr-vqa.github.io/

OK-VQA

www.okvqa.allenai.org

ST-VQA

www.rrc.cvc.uab.es/?ch=11

GQA

www.cs.stanford.edu/people/dorarad/gqa//about/html

TEXTVQA

www.textvqa.org/

VQA 3600

www.aliensunmin.github.io/project/360-VQA/

DOCVQA

www.docvqa.org

Appendix 2

figure a
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Yusuf, A.A., Chong, F. & Xianling, M. An analysis of graph convolutional networks and recent datasets for visual question answering. Artif Intell Rev 55, 6277–6300 (2022). https://doi.org/10.1007/s10462-022-10151-2

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