Pith. sign in

REVIEW 1 cited by

CrossA11y: Identifying Video Accessibility Issues via Cross-modal Grounding

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2208.11144 v1 pith:F4JUPRP2 submitted 2022-08-23 cs.HC

classification cs.HC
keywords accessibilitycrossa11yvideovisualaudioissuesvideosaccessible
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Authors make their videos visually accessible by adding audio descriptions (AD), and auditorily accessible by adding closed captions (CC). However, creating AD and CC is challenging and tedious, especially for non-professional describers and captioners, due to the difficulty of identifying accessibility problems in videos. A video author will have to watch the video through and manually check for inaccessible information frame-by-frame, for both visual and auditory modalities. In this paper, we present CrossA11y, a system that helps authors efficiently detect and address visual and auditory accessibility issues in videos. Using cross-modal grounding analysis, CrossA11y automatically measures accessibility of visual and audio segments in a video by checking for modality asymmetries. CrossA11y then displays these segments and surfaces visual and audio accessibility issues in a unified interface, making it intuitive to locate, review, script AD/CC in-place, and preview the described and captioned video immediately. We demonstrate the effectiveness of CrossA11y through a lab study with 11 participants, comparing to existing baseline.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Describe Now: User-Driven Audio Description for Blind and Low Vision Individuals

    cs.HC 2024-11 accept novelty 6.0 of 10

    On-demand, user-activated AI audio descriptions give blind and low vision viewers control over timing and detail of video descriptions, but they increase cognitive load and are preferred more for instructional than en...

Pith tools