Video Question Answering for People with Visual Impairments Using an Egocentric 360-Degree Camera
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914816511180800 |
|---|---|
| author | Song, Inpyo Joo, Minjun Kwon, Joonhyung Lee, Jangwon |
| author_facet | Song, Inpyo Joo, Minjun Kwon, Joonhyung Lee, Jangwon |
| contents | This paper addresses the daily challenges encountered by visually impaired individuals, such as limited access to information, navigation difficulties, and barriers to social interaction. To alleviate these challenges, we introduce a novel visual question answering dataset. Our dataset offers two significant advancements over previous datasets: Firstly, it features videos captured using a 360-degree egocentric wearable camera, enabling observation of the entire surroundings, departing from the static image-centric nature of prior datasets. Secondly, unlike datasets centered on singular challenges, ours addresses multiple real-life obstacles simultaneously through an innovative visual-question answering framework. We validate our dataset using various state-of-the-art VideoQA methods and diverse metrics. Results indicate that while progress has been made, satisfactory performance levels for AI-powered assistive services remain elusive for visually impaired individuals. Additionally, our evaluation highlights the distinctive features of the proposed dataset, featuring ego-motion in videos captured via 360-degree cameras across varied scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19794 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Video Question Answering for People with Visual Impairments Using an Egocentric 360-Degree Camera Song, Inpyo Joo, Minjun Kwon, Joonhyung Lee, Jangwon Computer Vision and Pattern Recognition This paper addresses the daily challenges encountered by visually impaired individuals, such as limited access to information, navigation difficulties, and barriers to social interaction. To alleviate these challenges, we introduce a novel visual question answering dataset. Our dataset offers two significant advancements over previous datasets: Firstly, it features videos captured using a 360-degree egocentric wearable camera, enabling observation of the entire surroundings, departing from the static image-centric nature of prior datasets. Secondly, unlike datasets centered on singular challenges, ours addresses multiple real-life obstacles simultaneously through an innovative visual-question answering framework. We validate our dataset using various state-of-the-art VideoQA methods and diverse metrics. Results indicate that while progress has been made, satisfactory performance levels for AI-powered assistive services remain elusive for visually impaired individuals. Additionally, our evaluation highlights the distinctive features of the proposed dataset, featuring ego-motion in videos captured via 360-degree cameras across varied scenarios. |
| title | Video Question Answering for People with Visual Impairments Using an Egocentric 360-Degree Camera |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.19794 |