Document detail
ID
oai:arXiv.org:2403.14783
Topic
Computer Science - Computer Vision... Computer Science - Artificial Inte... Computer Science - Computation and... Computer Science - Machine Learnin... Computer Science - Multiagent Syst...Author
Jiang, Bowen Zhuang, Zhijun Shivakumar, Shreyas S. Roth, Dan Taylor, Camillo J.Category
Computer Science
Year
2024
listing date
3/27/2024
Keywords
foundation models vqa multi-agent science computerAbstract
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.
;Comment: A full version of the paper will be released soon.
The codes are available at https://github.com/bowen-upenn/Multi-Agent-VQA
Jiang, Bowen,Zhuang, Zhijun,Shivakumar, Shreyas S.,Roth, Dan,Taylor, Camillo J., 2024, Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
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