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Open AccessJournal ArticleDOI

Guiding Novice Web Workers in Making Image Descriptions Using Templates

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TLDR
Two methods of employing novice Web workers to author descriptions of science, technology, engineering, and mathematics images to make them accessible to individuals with visual and print-reading disabilities are compared.
Abstract
This article compares two methods of employing novice Web workers to author descriptions of science, technology, engineering, and mathematics images to make them accessible to individuals with visual and print-reading disabilities. The goal is to identify methods of creating image descriptions that are inexpensive, effective, and follow established accessibility guidelines. The first method explicitly presented the guidelines to the worker, then the worker constructed the image description in an empty text box and table. The second method queried the worker for image information and then used responses to construct a template-based description according to established guidelines. The descriptions generated through queried image description (QID) were more likely to include information on the image category, title, caption, and units. They were also more similar to one another, based on Jaccard distances of q-grams, indicating that their word usage and structure were more standardized. Last, the workers preferred describing images using QID and found the task easier. Therefore, explicit instruction on image-description guidelines is not sufficient to produce quality image descriptions when using novice Web workers. Instead, it is better to provide information about images, then generate descriptions from responses using templates.

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Proceedings ArticleDOI

Understanding Blind People's Experiences with Computer-Generated Captions of Social Media Images

TL;DR: How blind and visually impaired people experience automatically generated captions in two studies about social media images is explored and the role of phrasing in encouraging trust or skepticism in captions is investigated.
Proceedings ArticleDOI

Rich Representations of Visual Content for Screen Reader Users

TL;DR: This work focuses on articulating the design space of representations of visual content for screen reader users, prototypes illustrating several points within this design space, and evaluations of several of these new image representations with people who are blind.
Proceedings ArticleDOI

"Person, Shoes, Tree. Is the Person Naked?" What People with Vision Impairments Want in Image Descriptions

TL;DR: 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.
Proceedings ArticleDOI

“It's almost like they're trying to hide it”: How User-Provided Image Descriptions Have Failed to Make Twitter Accessible

TL;DR: It is found that simply making it possible to provide image descriptions is not enough, and future directions for automated tools that may support users in writing high-quality descriptions are revealed.
Proceedings ArticleDOI

Going Beyond One-Size-Fits-All Image Descriptions to Satisfy the Information Wants of People Who are Blind or Have Low Vision

TL;DR: In this paper, the authors interviewed 28 blind or low vision individuals to learn how the scenario impacts what image content (information) should go into an image description, and offer their findings as a foundation for considering how to design next-generation image description technologies that can both (A) support a departure from one-size-fits-all image descriptions to context-aware descriptions, and (B) reveal what content to include in minimum viable descriptions for a large range of scenarios.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Proceedings ArticleDOI

Show and tell: A neural image caption generator

TL;DR: In this paper, a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation is proposed to generate natural sentences describing an image, which can be used to automatically describe the content of an image.
Posted Content

Show and Tell: A Neural Image Caption Generator

TL;DR: This paper presents a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image.
Proceedings ArticleDOI

Labeling images with a computer game

TL;DR: A new interactive system: a game that is fun and can be used to create valuable output that addresses the image-labeling problem and encourages people to do the work by taking advantage of their desire to be entertained.
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