mlsanityData to 7 Oct 2026

Vision Skills Needed to Answer Visual Questions

arXiv 2010.03160 ↗First posted 7 Oct 2020cs.HCcited by 17

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 3; 2024: 3; 2025: 4; 2026: 1.

17 papers.

Sort:

By area:

  1. 2022Grounding Answers for Visual Questions Asked by Visually Impaired Peoplecited by 47
  2. 2022PreSTU: Pre-Training for Scene-Text Understandingcited by 27
  3. 2021Separating Skills and Concepts for Novel Visual Question Answeringcited by 24
  4. 2024Long-Form Answers to Visual Questions from Blind and Low Vision Peoplecited by 18
  5. 2023VQA Therapy: Exploring Answer Differences by Visually Grounding Answerscited by 14
  6. 2022VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairmentscited by 11
  7. 2023Fully Authentic Visual Question Answering Dataset from Online Communitiescited by 9
  8. 2024Video Question Answering for People with Visual Impairments Using an Egocentric 360-Degree Cameracited by 9
  9. 2022Weakly Supervised Grounding for VQA in Vision-Language Transformerscited by 8
  10. 2023Salient Object Detection for Images Taken by People With Vision Impairmentscited by 7
  11. 2022What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?cited by 5
  12. 2024BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairmentscited by 5
5 more
  1. 2025"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Modelscited by 3
  2. 2025ROSA: Addressing text understanding challenges in photographs via ROtated SAmplingcited by 0
  3. 2025Not There Yet: Evaluating Vision Language Models in Simulating the Visual Perception of People with Low Visioncited by 0
  4. 2025Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Informationcited by 0
  5. 2026Touvigation: Embodied Adaptive Object Acquisition for Blind and Low-Vision Users in Unfamiliar Indoor Environmentscited by 0