Blind Users Accessing Their Training Images in Teachable Object Recognizers
Open accessAbstract
Teachable object recognizers provide a solution for a very practical need for blind people – instance level object recognition. They assume one can visually inspect the photos they provide for training, a critical and inaccessible step for those who are blind. In this work, we engineer data descriptors that address this challenge. They indicate in real time whether the object in the photo is cropped or too small, a hand is included, the photos is blurred, and how much photos vary from each other. Our descriptors are built into open source testbed iOS app, called MYCam. In a remote user study in (N = 12) blind participants’ homes, we show how descriptors, even when error-prone, support experimentation and have a positive impact in the quality of training set that can translate to model performance though this gain is not uniform. Participants found the app simple to use indicating that they could effectively train it and that the descriptors were useful. However, many found the training being tedious, opening discussions around the need for balance between information, time, and cognitive load.
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Annotating disability-centered datasets with meaningful 'ground truth' labels so that they can be used in benchmark studies and model evaluations remains a challenge, particularly when the phenomena of interest are inaccessible to the people who matter most.
“annotating these datasets with meaningful 'ground truth' labels so that they can be used in benchmark studies and model evaluations such as the present paper remains a challenge, particularly when the phenomena of interest are inaccessible to the people who matter most [51, 60].”
From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [60]
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Synthesizing and Evaluating a Visual Feedback Framework for Accessible Content Creation
Zhang, L., Hua, A., & Findlater, L. (2026) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3797867.3829025
Debugging Computer Vision as a Blind Researcher
Khan, R. R., & Hong, J. (2026) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3797867.3832769
Self-citation
- Authors
- Hong, J., Gandhi, J., Mensah, E. E., Zeraati, F. Z., Jarjue, E., Lee, K., & Kacorri, H.
- Affiliations
- Smith-Kettlewell Eye Research Institute (US)
- University of Maryland, College Park (US)
- Venue
- Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility
- Publisher
- ACM
- Published
- 2022-10-22
- Volume
- 2022
- Pages
- 1-18
- PMC
- PMC10008526
- OpenAlex
- W4307136615
- Cited by
- 22
- References
- 60
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 7 · 2023: 2
- FWCI
- 9.18
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- NSF (1816380)
- U.S. Dept. of Health & Human Services / ACL / NIDILRR (90REGE0008)
- Topics
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- blind users, data descriptors, iOS app, cognitive load, assistive technology
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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