VDMA: Video Question Answering with Dynamically Generated Multi-Agents
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Abstract (arXiv)
This technical report provides a detailed description of our approach to the EgoSchema Challenge 2024. The EgoSchema Challenge aims to identify the most appropriate responses to questions regarding a given video clip. In this paper, we propose Video Question Answering with Dynamically Generated Multi-Agents (VDMA). This method is a complementary approach to existing response generation systems by employing a multi-agent system with dynamically generated expert agents. This method aims to provide the most accurate and contextually appropriate responses. This report details the stages of our approach, the tools employed, and the results of our experiments.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 4 Jul 2024.
Builds on
5 of 6 references.
References · 5
- 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
- 2023EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understandingcited by 680
- 2024VideoAgent: Long-form Video Understanding with Large Language Model as Agentcited by 332
- 2022Learning Video Representations from Large Language Modelscited by 205
- 2023A Simple LLM Framework for Long-Range Video Question-Answeringcited by 178
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Citing papers per year: 2025: 2.
2 papers.