VTQAGen: BART-based Generative Model For Visual Text Question Answering-Reference-Cited by-同舟云学术

VTQAGen: BART-based Generative Model For Visual Text Question Answering

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Chen Haoru¹^ORCID,Wan Tianjiao²^ORCID,Lin Zhimin³^ORCID,Xu Kele²^ORCID,Wang Jin⁴^ORCID,Wang Huaimin²^ORCID

Affiliation:

1. Chongqing University of Posts and Telecommunication, Chongqing, China

2. National University of Defense Technology, Changsha, China

3. MindRank AI Ltd., Hangzhou, China

4. Chongqing University of Posts and Telecommunications, Chongqing, China

Funder

Major Science and Technology Innovation 2030 ``New Generation Artificial Intelligence' project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612844

Reference49 articles.

1. Aishwarya Agrawal , Jiasen Lu , Stanislaw Antol , Margaret Mitchell , Dhruv Batra , C Lawrence Zitnick , and Devi Parikh . 2017 . VQA: Visual Question Answering. In Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 21--29 . Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2017. VQA: Visual Question Answering. In Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 21--29.

2. Peter Anderson , Xiaodong He , Chris Buehler , Damien Teney , Mark Johnson , Stephen Gould , and Lei Zhang . 2018 . Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. In Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 6077--6086 . Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018. Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. In Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 6077--6086.

3. VQA: Visual Question Answering

4. LaTr: Layout-Aware Transformer for Scene-Text VQA

5. ICDAR 2019 Competition on Scene Text Visual Question Answering