ReGR: Relation-aware graph reasoning framework for video question answering-Reference-Cited by-同舟云学术

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ReGR: Relation-aware graph reasoning framework for video question answering

Published:2023-07 Issue:4 Volume:60 Page:103375
ISSN:0306-4573
Container-title:Information Processing & Management
language:en
Short-container-title:Information Processing & Management

Author:

Wang Zheng,Li Fangtao,Ota Kaoru,Dong Mianxiong,Wu Bin

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference53 articles.

1. Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., et al. (2018). Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings of the 2018 IEEE conference on computer vision and pattern recognition (pp. 6077–6086). Salt Lake City, USA: http://dx.doi.org/10.1109/CVPR.2018.00636.

2. Cai, J., Yuan, C., Shi, C., Li, L., Cheng, Y., & Shan, Y. (2020). Feature augmented memory with global attention network for videoqa. In Proceedings of the 2020 international joint conference on artificial intelligence (pp. 998–1004). Yokohama, Japan: http://dx.doi.org/10.24963/ijcai.2020/138.

3. Dang, L., Le, T., Le, V., & Tran, T. (2021). Hierarchical object-oriented spatio-temporal reasoning for video question answering. In Proceedings of the 2021 international joint conference on artificial intelligence (pp. 636–642). Virtual Online, Canada: http://dx.doi.org/10.24963/ijcai.2021/88.

4. Socially intelligent robots: dimensions of human-robot interaction;Dautenhahn;Philosophical Transactions of the Royal Society B,2007

5. Deep learning-based autonomous driving systems: a survey of attacks and defenses;Deng;IEEE Transactions on Industrial Informatics,2021

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