Open-Ended Multi-Modal Relational Reasoning for Video Question Answering
Abstract
In this paper, we introduce a robotic agent specifically designed to analyze external environments and address participants' questions. The primary focus of this agent is to assist individuals using language-based interactions within video-based scenes. Our proposed method integrates video recognition technology and natural language processing models within the robotic agent. We investigate the crucial factors affecting human-robot interactions by examining pertinent issues arising between participants and robot agents. Methodologically, our experimental findings reveal a positive relationship between trust and interaction efficiency. Furthermore, our model demonstrates a 2\% to 3\% performance enhancement in comparison to other benchmark methods.
Cite
@article{arxiv.2012.00822,
title = {Open-Ended Multi-Modal Relational Reasoning for Video Question Answering},
author = {Haozheng Luo and Ruiyang Qin and Chenwei Xu and Guo Ye and Zening Luo},
journal= {arXiv preprint arXiv:2012.00822},
year = {2024}
}
Comments
2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)