# Visual Question Answering: which investigated applications?

arXiv: https://arxiv.org/abs/2103.02937 · HTML: https://mlsanity.com/paper/2103.02937/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 62 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 25 of 25)

- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [PathVQA: 30000+ Questions for Medical Visual Question Answering](https://mlsanity.com/paper/2003.10286/) (2020)
- [Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering](https://mlsanity.com/paper/1712.00377/) (2017)
- [Visual Question Answering: A Survey of Methods and Datasets](https://mlsanity.com/paper/1607.05910/) (2016)
- [Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?](https://mlsanity.com/paper/1606.03556/) (2016)
- [Overcoming Data Limitation in Medical Visual Question Answering](https://mlsanity.com/paper/1909.11867/) (2019)
- [Automatic Understanding of Image and Video Advertisements](https://mlsanity.com/paper/1707.03067/) (2017)
- [Visual Question Answering: Datasets, Algorithms, and Future Challenges](https://mlsanity.com/paper/1610.01465/) (2016)
- [Why Does a Visual Question Have Different Answers?](https://mlsanity.com/paper/1908.04342/) (2019)
- [A Question-Centric Model for Visual Question Answering in Medical Imaging](https://mlsanity.com/paper/2003.08760/) (2020)
- [Visual Question Answering on 360deg Images](https://mlsanity.com/paper/2001.03339/) (2020)
- [Recommending Themes for Ad Creative Design via Visual-Linguistic Representations](https://mlsanity.com/paper/2001.07194/) (2020)
- [Describing Common Human Visual Actions in Images](https://mlsanity.com/paper/1506.02203/) (2015)
- [Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool](https://mlsanity.com/paper/1803.06936/) (2018)
- [Actively Seeking and Learning from Live Data](https://mlsanity.com/paper/1904.02865/) (2019)
- [Joint Video and Text Parsing for Understanding Events and Answering Queries](https://mlsanity.com/paper/1308.6628/) (2013)
- [Visual Question Answering for Cultural Heritage](https://mlsanity.com/paper/2003.09853/) (2020)
- [Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments](https://mlsanity.com/paper/1910.13249/) (2019)
- [Leveraging Medical Visual Question Answering with Supporting Facts](https://mlsanity.com/paper/1905.12008/) (2019)
- [From Visual to Acoustic Question Answering](https://mlsanity.com/paper/1902.11280/) (2019)
- [Which Ads to Show? Advertisement Image Assessment with Auxiliary Information via Multi-step Modality Fusion](https://mlsanity.com/paper/1910.02358/) (2019)
- [Visuo-Linguistic Question Answering (VLQA) Challenge](https://mlsanity.com/paper/2005.00330/) (2020)

## Built on by (most cited first, 11 of 11)

- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer](https://mlsanity.com/paper/2206.11053/) (2022)
- [Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2305.11692/) (2023)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [Visual Robustness Benchmark for Visual Question Answering (VQA)](https://mlsanity.com/paper/2407.03386/) (2024)
- [Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?](https://mlsanity.com/paper/2207.12101/) (2022)
- [QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning](https://mlsanity.com/paper/2205.03075/) (2022)
- [Visual Graph Question Answering with ASP and LLMs for Language Parsing](https://mlsanity.com/paper/2502.09211/) (2025)
- [SparrowVQE: Visual Question Explanation for Course Content Understanding](https://mlsanity.com/paper/2411.07516/) (2024)
- [An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images](https://mlsanity.com/paper/2504.21309/) (2025)
- [Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering](https://mlsanity.com/paper/2606.03269/) (2026)

## Abstract (arXiv)

Visual Question Answering (VQA) is an extremely stimulating and challenging research area where Computer Vision (CV) and Natural Language Processig (NLP) have recently met. In image captioning and video summarization, the semantic information is completely contained in still images or video dynamics, and it has only to be mined and expressed in a human-consistent way. Differently from this, in VQA semantic information in the same media must be compared with the semantics implied by a question expressed in natural language, doubling the artificial intelligence-related effort. Some recent surveys about VQA approaches have focused on methods underlying either the image-related processing or the verbal-related one, or on the way to consistently fuse the conveyed information. Possible applications are only suggested, and, in fact, most cited works rely on general-purpose datasets that are used to assess the building blocks of a VQA system. This paper rather considers the proposals that focus on real-world applications, possibly using as benchmarks suitable data bound to the application domain. The paper also reports about some recent challenges in VQA research.