# The Effect of Computer-Generated Descriptions on Photo-Sharing Experiences of People with Visual Impairments

arXiv: https://arxiv.org/abs/1805.01515 · HTML: https://mlsanity.com/paper/1805.01515/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

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

- [Computer Vision and Conflicting Values: Describing People with Automated Alt Text](https://mlsanity.com/paper/2105.12754/) (2021)
- [Salient Object Detection for Images Taken by People With Vision Impairments](https://mlsanity.com/paper/2301.05323/) (2023)
- [EditScribe: Non-Visual Image Editing with Natural Language Verification Loops](https://mlsanity.com/paper/2408.06632/) (2024)
- [A Dataset for Crucial Object Recognition in Blind and Low-Vision Individuals' Navigation](https://mlsanity.com/paper/2407.16777/) (2024)
- [Helping Visually Impaired People Take Better Quality Pictures](https://mlsanity.com/paper/2305.08066/) (2023)
- [Cripping AI: Reimagining AI Through Lived Disability Experiences](https://mlsanity.com/paper/2605.02080/) (2026)

## Abstract (arXiv)

Like sighted people, visually impaired people want to share photographs on social networking services, but find it difficult to identify and select photos from their albums. We aimed to address this problem by incorporating state-of-the-art computer-generated descriptions into Facebook's photo-sharing feature. We interviewed 12 visually impaired participants to understand their photo-sharing experiences and designed a photo description feature for the Facebook mobile application. We evaluated this feature with six participants in a seven-day diary study. We found that participants used the descriptions to recall and organize their photos, but they hesitated to upload photos without a sighted person's input. In addition to basic information about photo content, participants wanted to know more details about salient objects and people, and whether the photos reflected their personal aesthetics. We discuss these findings from the lens of self-disclosure and self-presentation theories and propose new computer vision research directions that will better support visual content sharing by visually impaired people.