"Person, Shoes, Tree. Is the Person Naked?" What People with Vision Impairments Want in Image Descriptions
By Abigale Stangl. 2020
156 citations recorded by the source corpus.
Access to digital images is important to people who are blind or have low vision (BLV). Many contemporary image description efforts do not take into account this population's nuanced image description preferences. In this paper, we present a qualitative study that provides insight into 28 BLV people's experiences with descriptions of digital images from news websites, social networking sites/platforms, eCommerce websites, employment websites, online dating websites/platforms, productivity applications, and e-publications. Our findings reveal how image description preferences vary based on the source where digital images are encountered and the surrounding context. We provide recommendations for the development of next-generation image description technologies inspired by our empirical analysis.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Cognitive Neuroscience
In Computer Vision and Pattern Recognition
- Angelina Wang
- Dora Zhao
- Olga Russakovsky
- Luc Van Gool
- Trevor Darrell
- Xiaogang Wang
- Andrea Vedaldi
- Dacheng Tao
In AI Ethics, Fairness & Society
Citations in the corpus
- Understanding and Evaluating Racial Biases in Image Captioning (2021, background): “Prior works [63, 51] fnd that, in certain contexts, people who are blind or low vision want racial descriptors to be included.”