A Design of Interface for Visual-Impaired People to Access Visual Information from Images Featuring Large Language Models and Visual Language Models-Reference-Cited by-同舟云学术

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A Design of Interface for Visual-Impaired People to Access Visual Information from Images Featuring Large Language Models and Visual Language Models

Published:2024-05-11 Issue: Volume: Page:1-4
ISSN:
Container-title:Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
language:
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Author:

Zhang Zhe-Xin¹^ORCID

Affiliation:

1. Digital Nature Group, Institute of Library, Information and Media Science, University of Tsukuba, Japan

Funder

Core Research for Evolutional Science and Technology

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3613905.3648648

Reference5 articles.

1. Jiaqi Chen Zeyu Yang and Li Zhang. 2023. Semantic Segment Anything. https://github.com/fudan-zvg/Semantic-Segment-Anything.

2. Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, 2023. Segment anything. arXiv preprint arXiv:2304.02643 (2023).

3. Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, and Jianfeng Gao. 2023. Semantic-SAM: Segment and Recognize Anything at Any Granularity. arXiv preprint arXiv:2307.04767 (2023).

4. Understanding Visual Arts Experiences of Blind People

5. Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, and Jianfeng Gao. 2023. Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V. arXiv preprint arXiv:2310.11441 (2023).

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