Caption: Generating Informative Content Labels for Image Buttons Using Next-Screen Context

We present Caption, an LLM-powered content label generation tool for visual interactive elements on mobile devices.Content labels are essential for screen readers to provide announcements for image-based elements, but are often missing or uninformative due to developer neglect.Automated captioning systems attempt to address this, but are limited to on-screen context, often resulting in inaccurate or unspecific labels.To generate more accurate and descriptive labels, Caption collects next-screen context on interactive elements by navigating to the destination screen that appears after an interaction and incorporating information from both the origin and destination screens.Preliminary results show Caption generates more accurate labels than both human annotators and an LLM baseline.We expect Caption to empower developers by providing actionable accessibility suggestions and directly support on-demand repairs by screen reader users.

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Publication Details

Published
2025-09-27
DOI
https://doi.org/10.1145/3746058.3758413
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Caption: Generating Informative Content Labels for Image Buttons Using Next-Screen Context

Mingyuan Zhong, Qing Nie, Ajit Mallavarapu
Multimodal Machine Learning Applications
article

Caption: Generating Informative Content Labels for Image Buttons Using Next-Screen Context

Mingyuan Zhong, Qing Nie, Ajit Mallavarapu
article en

Abstract

We present Caption, an LLM-powered content label generation tool for visual interactive elements on mobile devices.Content labels are essential for screen readers to provide announcements for image-based elements, but are often missing or uninformative due to developer neglect.Automated captioning systems attempt to address this, but are limited to on-screen context, often resulting in inaccurate or unspecific labels.To generate more accurate and descriptive labels, Caption collects next-screen context on interactive elements by navigating to the destination screen that appears after an interaction and incorporating information from both the origin and destination screens.Preliminary results show Caption generates more accurate labels than both human annotators and an LLM baseline.We expect Caption to empower developers by providing actionable accessibility suggestions and directly support on-demand repairs by screen reader users.

University of Washington (US)
Openalex Percentile: Top 61%
Multimodal Machine Learning Applications
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