ViDscribe: Multimodal AI for Customizing Audio Description and Question Answering in Online Videos
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910053970214912 |
|---|---|
| author | Cheema, Maryam Elahimanesh, Sina Fazli, Pooyan Seifi, Hasti |
| author_facet | Cheema, Maryam Elahimanesh, Sina Fazli, Pooyan Seifi, Hasti |
| contents | Advances in multimodal large language models enable automatic video narration and question answering (VQA), offering scalable alternatives to labor-intensive, human-authored audio descriptions (ADs) for blind and low vision (BLV) viewers. However, prior AI-driven AD systems rarely adapt to the diverse needs and preferences of BLV individuals across videos and are typically evaluated in controlled, single-session settings. We present ViDscribe, a web-based platform that integrates AI-generated ADs with six types of user customizations and a conversational VQA interface for YouTube videos. Through a longitudinal, in-the-wild study with eight BLV participants, we examine how users engage with customization and VQA features over time. Our results show sustained engagement with both features and that customized ADs improve effectiveness, enjoyment, and immersion compared to default ADs, highlighting the value of personalized, interactive video access for BLV users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14662 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ViDscribe: Multimodal AI for Customizing Audio Description and Question Answering in Online Videos Cheema, Maryam Elahimanesh, Sina Fazli, Pooyan Seifi, Hasti Human-Computer Interaction Advances in multimodal large language models enable automatic video narration and question answering (VQA), offering scalable alternatives to labor-intensive, human-authored audio descriptions (ADs) for blind and low vision (BLV) viewers. However, prior AI-driven AD systems rarely adapt to the diverse needs and preferences of BLV individuals across videos and are typically evaluated in controlled, single-session settings. We present ViDscribe, a web-based platform that integrates AI-generated ADs with six types of user customizations and a conversational VQA interface for YouTube videos. Through a longitudinal, in-the-wild study with eight BLV participants, we examine how users engage with customization and VQA features over time. Our results show sustained engagement with both features and that customized ADs improve effectiveness, enjoyment, and immersion compared to default ADs, highlighting the value of personalized, interactive video access for BLV users. |
| title | ViDscribe: Multimodal AI for Customizing Audio Description and Question Answering in Online Videos |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.14662 |