mlsanityData to 7 Oct 2026

A survey on VQA_Datasets and Approaches

arXiv 2105.00421 ↗First posted 2 May 2021cs.CV cs.AI cs.LGcited by 11

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Abstract (arXiv)

Visual question answering (VQA) is a task that combines both the techniques of computer vision and natural language processing. It requires models to answer a text-based question according to the information contained in a visual. In recent years, the research field of VQA has been expanded. Research that focuses on the VQA, examining the reasoning ability and VQA on scientific diagrams, has also been explored more. Meanwhile, more multimodal feature fusion mechanisms have been proposed. This paper will review and analyze existing datasets, metrics, and models proposed for the VQA task.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 49 of its 61 references that are in the map. First posted on arXiv 2 May 2021.

Builds on

49 of 61 references.

References · 49

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37 more
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Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 5; 2025: 2.

11 papers.

  1. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  2. 2023Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literaturecited by 20
  3. 2024Transfer Learning in Human Activity Recognition: A Surveycited by 14
  4. 2023Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Frameworkcited by 11
  5. 2023Graph Neural Networks in Vision-Language Image Understanding: A Surveycited by 10
  6. 2025Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Taskscited by 7
  7. 2024DocTabQA: Answering Questions from Long Documents Using Tablescited by 6
  8. 2024Data Processing Techniques for Modern Multimodal Modelscited by 3
  9. 2024Everything is a Video: Unifying Modalities through Next-Frame Predictioncited by 2
  10. 2025BLaVe-CoT: Consistency-Aware Visual Question Answering for Blind and Low Vision Userscited by 1
  11. 2023Knowledge-Based Counterfactual Queries for Visual Question Answeringcited by 0