# Visual Question Answering using Deep Learning: A Survey and Performance Analysis

arXiv: https://arxiv.org/abs/1909.01860 · HTML: https://mlsanity.com/paper/1909.01860/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 19 of 19)

- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [Grounding Answers for Visual Questions Asked by Visually Impaired People](https://mlsanity.com/paper/2202.01993/) (2022)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [A Review of Emerging Research Directions in Abstract Visual Reasoning](https://mlsanity.com/paper/2202.10284/) (2022)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [Enhancing Visual Question Answering through Question-Driven Image Captions as Prompts](https://mlsanity.com/paper/2404.08589/) (2024)
- [Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework](https://mlsanity.com/paper/2307.12626/) (2023)
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- [A Comprehensive Review of the Video-to-Text Problem](https://mlsanity.com/paper/2103.14785/) (2021)
- [Evaluation of Question Answering Systems: Complexity of judging a natural language](https://mlsanity.com/paper/2209.12617/) (2022)
- [Generating Context-Aware Natural Answers for Questions in 3D Scenes](https://mlsanity.com/paper/2310.19516/) (2023)
- [COIN: Counterfactual Image Generation for VQA Interpretation](https://mlsanity.com/paper/2201.03342/) (2022)
- [Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions](https://mlsanity.com/paper/2211.02646/) (2022)
- [ImageChain: Advancing Sequential Image-to-Text Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2502.19409/) (2025)
- [3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding](https://mlsanity.com/paper/2402.17983/) (2024)
- [Interpreting Vision and Language Generative Models with Semantic Visual Priors](https://mlsanity.com/paper/2304.14986/) (2023)
- [Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths](https://mlsanity.com/paper/2203.02295/) (2022)
- [Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review](https://mlsanity.com/paper/2407.00252/) (2024)

## Abstract (arXiv)

The Visual Question Answering (VQA) task combines challenges for processing data with both Visual and Linguistic processing, to answer basic `common sense' questions about given images. Given an image and a question in natural language, the VQA system tries to find the correct answer to it using visual elements of the image and inference gathered from textual questions. In this survey, we cover and discuss the recent datasets released in the VQA domain dealing with various types of question-formats and robustness of the machine-learning models. Next, we discuss about new deep learning models that have shown promising results over the VQA datasets. At the end, we present and discuss some of the results computed by us over the vanilla VQA model, Stacked Attention Network and the VQA Challenge 2017 winner model. We also provide the detailed analysis along with the challenges and future research directions.