mlsanityData to 14 Oct 2026

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

arXiv 2210.14966 ↗First posted 26 Oct 2022cs.CL cs.AI cs.CVcited by 6

Not on the map. Why?

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.

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

15 of 23 references.

References · 15

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  3. 2015VQA: Visual Question Answeringcited by 2,506
  4. 2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,619
  5. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,312
  6. 2019OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledgecited by 1,121
  7. 2018VizWiz Grand Challenge: Answering Visual Questions from Blind Peoplecited by 858
  8. 2019Deep Modular Co-Attention Networks for Visual Question Answeringcited by 339
  9. 2018Bilinear Attention Networkscited by 280
  10. 2017Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answeringcited by 117
  11. 2014A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Inputcited by 112
  12. 2017Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answeringcited by 79
3 more
  1. 2018Pythia v0.1: the Winning Entry to the VQA Challenge 2018cited by 62
  2. 2019Why Does a Visual Question Have Different Answers?cited by 34
  3. 2020Vision Skills Needed to Answer Visual Questionscited by 17

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 1.

6 papers.

  1. 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performancecited by 26
  2. 2023Context-VQA: Towards Context-Aware and Purposeful Visual Question Answeringcited by 6
  3. 2024Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challengescited by 5
  4. 2025"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Modelscited by 3
  5. 2024AccessShare: Co-designing Data Access and Sharing with Blind Peoplecited by 0
  6. 2026Characterizing the Performance Gap in Human Activity Recognition for Older Adultscited by 0