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VDMA: Video Question Answering with Dynamically Generated Multi-Agents

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arxiv 2407.03610 v1 pith:HIJPYZWF submitted 2024-07-04 cs.CV

Noriyuki Kugo , Tatsuya Ishibashi , Kosuke Ono , Yuji Sato This is my paper · ORCID
classification cs.CV
keywords approachdynamicallygeneratedvideoaimsansweringappropriatechallenge
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. DIVE: Deep-search Iterative Video Exploration A Technical Report for the CVRR Challenge at CVPR 2025

    cs.CV 2025-06 conditional novelty 5.0 of 10

    DIVE, an iterative question-decomposition system with intent estimation and object-centric video summarization, achieves 81.44% on CVRR-ES.

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