Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916171141349376 |
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| author | Jiang, Bowen Zhuang, Zhijun Shivakumar, Shreyas S. Roth, Dan Taylor, Camillo J. |
| author_facet | Jiang, Bowen Zhuang, Zhijun Shivakumar, Shreyas S. Roth, Dan Taylor, Camillo J. |
| contents | This work explores the zero-shot capabilities of foundation models in Visual Question Answering (VQA) tasks. We propose an adaptive multi-agent system, named Multi-Agent VQA, to overcome the limitations of foundation models in object detection and counting by using specialized agents as tools. Unlike existing approaches, our study focuses on the system's performance without fine-tuning it on specific VQA datasets, making it more practical and robust in the open world. We present preliminary experimental results under zero-shot scenarios and highlight some failure cases, offering new directions for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14783 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering Jiang, Bowen Zhuang, Zhijun Shivakumar, Shreyas S. Roth, Dan Taylor, Camillo J. Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning Multiagent Systems This work explores the zero-shot capabilities of foundation models in Visual Question Answering (VQA) tasks. We propose an adaptive multi-agent system, named Multi-Agent VQA, to overcome the limitations of foundation models in object detection and counting by using specialized agents as tools. Unlike existing approaches, our study focuses on the system's performance without fine-tuning it on specific VQA datasets, making it more practical and robust in the open world. We present preliminary experimental results under zero-shot scenarios and highlight some failure cases, offering new directions for future research. |
| title | Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2403.14783 |