mlsanityData to 7 Oct 2026

Visual Question Answering: A Survey of Methods and Datasets

arXiv 1607.05910 ↗First posted 20 Jul 2016cs.CVcited by 123

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Cited by 30 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models.

Key facts. Cited by 123 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2016.

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Citing papers per year: 2021: 18; 2022: 26; 2023: 25; 2024: 22; 2025: 21; 2026: 11.

123 papers.

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  1. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 641
  2. 2022VLP: A Survey on Vision-Language Pre-trainingcited by 159
  3. 2021Medical Visual Question Answering: A Surveycited by 93
  4. 2022MapQA: A Dataset for Question Answering on Choropleth Mapscited by 82
  5. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  6. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  7. 2022Grounding Answers for Visual Questions Asked by Visually Impaired Peoplecited by 47
  8. 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
  9. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  10. 2022Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Videocited by 43
  11. 2024Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignmentcited by 42
  12. 2021COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentencescited by 40
38 more
  1. 2022From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Datacited by 38
  2. 2023Visual cognition in multimodal large language modelscited by 38
  3. 2025PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Trainingcited by 37
  4. 2022A Review of Emerging Research Directions in Abstract Visual Reasoningcited by 33
  5. 2025CAFe: Unifying Representation and Generation with Contrastive-Autoregressive Finetuningcited by 31
  6. 2021Change Detection Meets Visual Question Answeringcited by 29
  7. 2022A Review on Methods and Applications in Multimodal Deep Learningcited by 29
  8. 2022(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answeringcited by 29
  9. 2023Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applicationscited by 27
  10. 2021Recent Advances and Trends in Multimodal Deep Learning: A Reviewcited by 26
  11. 2023AR2-D2:Training a Robot Without a Robotcited by 26
  12. 2021Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledgecited by 24
  13. 2022Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answeringcited by 24
  14. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunitiescited by 24
  15. 2023Multimodality of AI for Education: Towards Artificial General Intelligencecited by 21
  16. 2022Watching the News: Towards VideoQA Models that can Readcited by 20
  17. 2022Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imaginationcited by 18
  18. 2024Long-Form Answers to Visual Questions from Blind and Low Vision Peoplecited by 18
  19. 2021Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matricescited by 17
  20. 2023Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learningcited by 16
  21. 2024Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Modelscited by 16
  22. 2024EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answeringcited by 15
  23. 2024Improving Generalization in Visual Reasoning via Self-Ensemblecited by 15
  24. 2021Cognitive Visual Commonsense Reasoning Using Dynamic Working Memorycited by 14
  25. 2022VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challengescited by 14
  26. 2021AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Mediacited by 13
  27. 2024Exploring Phrase-Level Grounding with Text-to-Image Diffusion Modelcited by 13
  28. 2021Interpretable Visual Understanding with Cognitive Attention Networkcited by 12
  29. 2023Learning Differentiable Logic Programs for Abstract Visual Reasoningcited by 12
  30. 2025DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Modelscited by 12
  31. 2021Visual Question Answering: which investigated applications?cited by 11
  32. 2022Joint Answering and Explanation for Visual Commonsense Reasoningcited by 11
  33. 2023A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Cluescited by 11
  34. 2023Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Frameworkcited by 11
  35. 2022What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learningcited by 10
  36. 2022Visual Recognition by Requestcited by 10
  37. 2021Answer Questions with Right Image Regions: A Visual Attention Regularization Approachcited by 9
  38. 2023Fully Authentic Visual Question Answering Dataset from Online Communitiescited by 9

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