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

Source: [Open-Ended Multi-Modal Relational Reasoning for Video Question Answering](<https://www.sota2.com/research/paper/68fa738a1e209131cea5d118>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:52:14.378Z

Authors: Haozheng Luo, Ruiyang Qin, Chenwei Xu, Guo Ye, Zening Luo

Published: 2020-12-01T20:49:59.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.00822>)

[PDF](<https://arxiv.org/pdf/2012.00822.pdf>)