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arXiv · 2012.00822

Open-Ended Multi-Modal Relational Reasoning for Video Question Answering

Haozheng Luo·Ruiyang Qin·Chenwei Xu·Guo Ye·Zening Luo
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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.

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Haozheng Luo, Ruiyang Qin, Chenwei Xu, Guo Ye, Zening Luo. 2020-12-01. Open-Ended Multi-Modal Relational Reasoning for Video Question Answering. https://doi.org/10.1109/ro-man57019.2023.10309342

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