# Vision Skills Needed to Answer Visual Questions

arXiv: https://arxiv.org/abs/2010.03160 · HTML: https://mlsanity.com/paper/2010.03160/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [Grounding Answers for Visual Questions Asked by Visually Impaired People](https://mlsanity.com/paper/2202.01993/) (2022)
- [PreSTU: Pre-Training for Scene-Text Understanding](https://mlsanity.com/paper/2209.05534/) (2022)
- [Separating Skills and Concepts for Novel Visual Question Answering](https://mlsanity.com/paper/2107.09106/) (2021)
- [Long-Form Answers to Visual Questions from Blind and Low Vision People](https://mlsanity.com/paper/2408.06303/) (2024)
- [VQA Therapy: Exploring Answer Differences by Visually Grounding Answers](https://mlsanity.com/paper/2308.11662/) (2023)
- [VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairments](https://mlsanity.com/paper/2207.11810/) (2022)
- [Fully Authentic Visual Question Answering Dataset from Online Communities](https://mlsanity.com/paper/2311.15562/) (2023)
- [Video Question Answering for People with Visual Impairments Using an Egocentric 360-Degree Camera](https://mlsanity.com/paper/2405.19794/) (2024)
- [Weakly Supervised Grounding for VQA in Vision-Language Transformers](https://mlsanity.com/paper/2207.02334/) (2022)
- [Salient Object Detection for Images Taken by People With Vision Impairments](https://mlsanity.com/paper/2301.05323/) (2023)
- [What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?](https://mlsanity.com/paper/2210.14966/) (2022)
- [BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairments](https://mlsanity.com/paper/2407.18243/) (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)
- [ROSA: Addressing text understanding challenges in photographs via ROtated SAmpling](https://mlsanity.com/paper/2506.03665/) (2025)
- [Not There Yet: Evaluating Vision Language Models in Simulating the Visual Perception of People with Low Vision](https://mlsanity.com/paper/2508.10972/) (2025)
- [Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Information](https://mlsanity.com/paper/2512.10102/) (2025)
- [Touvigation: Embodied Adaptive Object Acquisition for Blind and Low-Vision Users in Unfamiliar Indoor Environments](https://mlsanity.com/paper/2609.21828/) (2026)

## Abstract (arXiv)

The task of answering questions about images has garnered attention as a practical service for assisting populations with visual impairments as well as a visual Turing test for the artificial intelligence community. Our first aim is to identify the common vision skills needed for both scenarios. To do so, we analyze the need for four vision skills---object recognition, text recognition, color recognition, and counting---on over 27,000 visual questions from two datasets representing both scenarios. We next quantify the difficulty of these skills for both humans and computers on both datasets. Finally, we propose a novel task of predicting what vision skills are needed to answer a question about an image. Our results reveal (mis)matches between aims of real users of such services and the focus of the AI community. We conclude with a discussion about future directions for addressing the visual question answering task.