Open-Ended Multi-Modal Relational Reasoning for Video Question Answering
This work aims to improve human-robot interaction efficiency for individuals using language-based interactions within video-based scenes, showing an incremental gain.
This paper introduces a robotic agent that analyzes video environments and answers user questions. The agent integrates video recognition and natural language processing, demonstrating a 2% to 3% performance enhancement over benchmark methods.
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.