mlsanityData to 7 Oct 2026

Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision People

arXiv 2403.15604 ↗First posted 22 Mar 2024cs.HC cs.AIcited by 24

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Abstract (arXiv)

"Scene description" applications that describe visual content in a photo are useful daily tools for blind and low vision (BLV) people. Researchers have studied their use, but they have only explored those that leverage remote sighted assistants; little is known about applications that use AI to generate their descriptions. Thus, to investigate their use cases, we conducted a two-week diary study where 16 BLV participants used an AI-powered scene description application we designed. Through their diary entries and follow-up interviews, users shared their information goals and assessments of the visual descriptions they received. We analyzed the entries and found frequent use cases, such as identifying visual features of known objects, and surprising ones, such as avoiding contact with dangerous objects. We also found users scored the descriptions relatively low on average, 2.76 out of 5 (SD=1.49) for satisfaction and 2.43 out of 4 (SD=1.16) for trust, showing that descriptions still need significant improvements to deliver satisfying and trustworthy experiences. We discuss future opportunities for AI as it becomes a more powerful accessibility tool for BLV users.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 8 of its 43 references that are in the map. First posted on arXiv 22 Mar 2024.

Builds on

8 of 43 references.

References · 8

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  3. 2018VizWiz Grand Challenge: Answering Visual Questions from Blind Peoplecited by 852
  4. 2016SPICE: Semantic Propositional Image Caption Evaluationcited by 820
  5. 2015YFCC100M: The New Data in Multimedia Researchcited by 234
  6. 2022Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metricscited by 18
  7. 2018Conversations for Vision: Remote Sighted Assistants Helping People with Visual Impairmentscited by 6
  8. 2023ImageAssist: Tools for Enhancing Touchscreen-Based Image Exploration Systems for Blind and Low Vision Userscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 9; 2026: 11.

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  1. 2025Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Modelscited by 13
  2. 2025Towards Understanding the Use of MLLM-Enabled Applications for Visual Interpretation by Blind and Low Vision Peoplecited by 11
  3. 2024Vision-Language Models under Cultural and Inclusive Considerationscited by 8
  4. 2025"Before, I Asked My Mom, Now I Ask ChatGPT": Visual Privacy Management with Generative AI for Blind and Low-Vision Peoplecited by 6
  5. 2025Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptionscited by 6
  6. 2024Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challengescited by 5
  7. 2025StreetReaderAI: Making Street View Accessible Using Context-Aware Multimodal AIcited by 5
  8. 2024Engaging with Children's Artwork in Mixed Visual-Ability Familiescited by 3
  9. 2025"I Can See Forever!": Evaluating Real-time VideoLLMs for Assisting Individuals with Visual Impairmentscited by 3
  10. 2025"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Modelscited by 3
  11. 2026How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision Peoplecited by 3
  12. 2024NaVIP: An Image-Centric Indoor Navigation Solution for Visually Impaired Peoplecited by 2
12 more
  1. 2025Situat3DChange: Situated 3D Change Understanding Dataset for Multimodal Large Language Modelcited by 2
  2. 2026Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Userscited by 2
  3. 2026Finding the Signal in the Noise: An Exploratory Study on Assessing the Effectiveness of AI and Accessibility Forums for Blind Users' Support Needscited by 1
  4. 2025Guiding Multimodal Large Language Models with Blind and Low Vision People Visual Questions for Proactive Visual Interpretationscited by 0
  5. 2026Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Playercited by 0
  6. 2026Lost in Instructions: Study of Blind Users' Experiences with DIY Manuals and AI-Rewritten Instructions for Assembly, Operation, and Troubleshooting of Tangible Productscited by 0
  7. 2026Understanding the Use of a Large Language Model-Powered Guide to Make Virtual Reality Accessible for Blind and Low Vision Peoplecited by 0
  8. 2026Beyond Descriptions: A Generative Scene2Audio Framework for Blind and Low-Vision Users to Experience Vista Landscapescited by 0
  9. 2026Chart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualizationcited by 0
  10. 2026Navigation Alone Is Not Enough: Evaluating Explanatory Assistive UI Agentscited by 0
  11. 2026RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Visioncited by 0
  12. 2026Touvigation: Embodied Adaptive Object Acquisition for Blind and Low-Vision Users in Unfamiliar Indoor Environmentscited by 0