# Visual Question Answering: Datasets, Algorithms, and Future Challenges

arXiv: https://arxiv.org/abs/1610.01465 · HTML: https://mlsanity.com/paper/1610.01465/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2016.

## Built on by (most cited first, 30 of 56)

- [VLP: A Survey on Vision-Language Pre-training](https://mlsanity.com/paper/2202.09061/) (2022)
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Learning to Predict Visual Attributes in the Wild](https://mlsanity.com/paper/2106.09707/) (2021)
- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly](https://mlsanity.com/paper/2204.13631/) (2022)
- [Clotho-AQA: A Crowdsourced Dataset for Audio Question Answering](https://mlsanity.com/paper/2204.09634/) (2022)
- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [Unleashing the potential of prompt engineering for large language models](https://mlsanity.com/paper/2310.14735/) (2023)
- [A Review of Emerging Research Directions in Abstract Visual Reasoning](https://mlsanity.com/paper/2202.10284/) (2022)
- [BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency](https://mlsanity.com/paper/2303.12419/) (2023)
- [Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval](https://mlsanity.com/paper/2505.19650/) (2025)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [Robust Visual Question Answering: Datasets, Methods, and Future Challenges](https://mlsanity.com/paper/2307.11471/) (2023)
- [Multimodality of AI for Education: Towards Artificial General Intelligence](https://mlsanity.com/paper/2312.06037/) (2023)
- [Explaining Autonomous Driving Actions with Visual Question Answering](https://mlsanity.com/paper/2307.10408/) (2023)
- [OmniCount: Multi-label Object Counting with Semantic-Geometric Priors](https://mlsanity.com/paper/2403.05435/) (2024)
- [How Modular Should Neural Module Networks Be for Systematic Generalization?](https://mlsanity.com/paper/2106.08170/) (2021)
- [EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering](https://mlsanity.com/paper/2410.20263/) (2024)
- [VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges](https://mlsanity.com/paper/2212.13296/) (2022)
- [Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model](https://mlsanity.com/paper/2407.05352/) (2024)
- [Selective Replay Enhances Learning in Online Continual Analogical Reasoning](https://mlsanity.com/paper/2103.03987/) (2021)
- [Globally Gated Deep Linear Networks](https://mlsanity.com/paper/2210.17449/) (2022)
- [HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language](https://mlsanity.com/paper/2305.17690/) (2023)
- [Visual Question Answering: which investigated applications?](https://mlsanity.com/paper/2103.02937/) (2021)
- [CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings](https://mlsanity.com/paper/2111.07993/) (2021)
- [FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector](https://mlsanity.com/paper/2412.05293/) (2024)
- [Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions](https://mlsanity.com/paper/2304.03147/) (2023)
- [Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods](https://mlsanity.com/paper/2105.14875/) (2021)
- [CoralVQA: A Large-Scale Visual Question Answering Dataset for Coral Reef Image Understanding](https://mlsanity.com/paper/2507.10449/) (2025)
- [Fairness in AI Systems: Mitigating gender bias from language-vision models](https://mlsanity.com/paper/2305.01888/) (2023)

## Abstract (arXiv)

Visual Question Answering (VQA) is a recent problem in computer vision and natural language processing that has garnered a large amount of interest from the deep learning, computer vision, and natural language processing communities. In VQA, an algorithm needs to answer text-based questions about images. Since the release of the first VQA dataset in 2014, additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, we discuss the limitations of current datasets with regard to their ability to properly train and assess VQA algorithms. We then exhaustively review existing algorithms for VQA. Finally, we discuss possible future directions for VQA and image understanding research.