mlsanityData to 14 Oct 2026

VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges

arXiv 2212.13296 ↗First posted 26 Dec 2022cs.CVcited by 14

On the map: LLM reasoning → Multimodal reasoning

Abstract (arXiv)

Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that employs artificial neural network concepts, has enabled the most rapid growth in these domains. The integration of vision and language has sparked a lot of attention as a result of this. The tasks have been created in such a way that they properly exemplify the concepts of deep learning. In this review paper, we provide a thorough and an extensive review of the state of the arts approaches, key models design principles and discuss existing datasets, methods, their problem formulation and evaluation measures for VQA and Visual reasoning tasks to understand vision and language representation learning. We also present some potential future paths in this field of research, with the hope that our study may generate new ideas and novel approaches to handle existing difficulties and develop new applications.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 104 of its 153 references that are in the map. First posted on arXiv 26 Dec 2022.

Builds on

104 of 153 references.

References · 104

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38 more
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50 most cited of 104 All 104 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 5; 2026: 2.

14 papers.

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  1. 2023Large Language Models are Visual Reasoning Coordinatorscited by 58
  2. 2024From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understandingcited by 30
  3. 2025DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruningcited by 20
  4. 2024Improving Generalization in Visual Reasoning via Self-Ensemblecited by 15
  5. 2025Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Taskscited by 7
  6. 2026H2HMem: A Multimodal Memory Benchmark for Agents in Human-Human Interactionscited by 6
  7. 2024I Know About "Up"! Enhancing Spatial Reasoning in Visual Language Models Through 3D Reconstructioncited by 4
  8. 2024Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoningcited by 1
  9. 2024Can VLMs be used on videos for action recognition? LLMs are Visual Reasoning Coordinatorscited by 1
  10. 2025VIKSER: Visual Knowledge-Driven Self-Reinforcing Reasoning Frameworkcited by 1
  11. 2025Multimodal AI for Gastrointestinal Diagnostics: Tackling VQA in MEDVQA-GI 2025cited by 1
  12. 2025Constructing and Interpreting Digital Twin Representations for Visual Reasoning via Reinforcement Learningcited by 1
2 more
  1. 2024Cross-Modal Coordination Across a Diverse Set of Input Modalitiescited by 0
  2. 2026Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering
  2. 2022Vision-Language Intelligence: Tasks, Representation Learning, and Large Models
  3. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
  4. 2025Visual question answering: from early developments to recent advances -- a survey
  5. 2022Deep Neural Networks for Visual Reasoning
  6. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities
  7. 2025Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison
  8. 2026VRIQ: Benchmarking and Analyzing Visual-Reasoning IQ of VLMs