# What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?

arXiv: https://arxiv.org/abs/2210.14966 · HTML: https://mlsanity.com/paper/2210.14966/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Deep Modular Co-Attention Networks for Visual Question Answering](https://mlsanity.com/paper/1906.10770/) (2019)
- [Bilinear Attention Networks](https://mlsanity.com/paper/1805.07932/) (2018)
- [Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering](https://mlsanity.com/paper/1708.01471/) (2017)
- [A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input](https://mlsanity.com/paper/1410.0210/) (2014)
- [Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering](https://mlsanity.com/paper/1708.03619/) (2017)
- [Pythia v0.1: the Winning Entry to the VQA Challenge 2018](https://mlsanity.com/paper/1807.09956/) (2018)
- [Why Does a Visual Question Have Different Answers?](https://mlsanity.com/paper/1908.04342/) (2019)
- [Vision Skills Needed to Answer Visual Questions](https://mlsanity.com/paper/2010.03160/) (2020)

## Built on by (most cited first, 6 of 6)

- [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://mlsanity.com/paper/2305.09022/) (2023)
- [Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering](https://mlsanity.com/paper/2307.15745/) (2023)
- [Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges](https://mlsanity.com/paper/2408.03303/) (2024)
- ["It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models](https://mlsanity.com/paper/2511.08917/) (2025)
- [AccessShare: Co-designing Data Access and Sharing with Blind People](https://mlsanity.com/paper/2407.19351/) (2024)
- [Characterizing the Performance Gap in Human Activity Recognition for Older Adults](https://mlsanity.com/paper/2610.02711/) (2026)

## Abstract (arXiv)

In visual question answering (VQA), a machine must answer a question given an associated image. Recently, accessibility researchers have explored whether VQA can be deployed in a real-world setting where users with visual impairments learn about their environment by capturing their visual surroundings and asking questions. However, most of the existing benchmarking datasets for VQA focus on machine "understanding" and it remains unclear how progress on those datasets corresponds to improvements in this real-world use case. We aim to answer this question by evaluating discrepancies between machine "understanding" datasets (VQA-v2) and accessibility datasets (VizWiz) by evaluating a variety of VQA models. Based on our findings, we discuss opportunities and challenges in VQA for accessibility and suggest directions for future work.