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

arXiv: https://arxiv.org/abs/2012.00822 · HTML: https://mlsanity.com/paper/2012.00822/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices](https://mlsanity.com/paper/2406.03777/) (2024)
- [Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge](https://mlsanity.com/paper/2411.13766/) (2024)

## Abstract (arXiv)

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.