VDMA: Video Question Answering with Dynamically Generated Multi-Agents
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914858057859072 |
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| author | Kugo, Noriyuki Ishibashi, Tatsuya Ono, Kosuke Sato, Yuji |
| author_facet | Kugo, Noriyuki Ishibashi, Tatsuya Ono, Kosuke Sato, Yuji |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03610 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VDMA: Video Question Answering with Dynamically Generated Multi-Agents Kugo, Noriyuki Ishibashi, Tatsuya Ono, Kosuke Sato, Yuji Computer Vision and Pattern Recognition 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. |
| title | VDMA: Video Question Answering with Dynamically Generated Multi-Agents |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.03610 |