Summary of Multi-agent Vqa: Exploring Multi-agent Foundation Models in Zero-shot Visual Question Answering, by Bowen Jiang et al.
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
by Bowen Jiang, Zhijun Zhuang, Shreyas S. Shivakumar, Dan Roth, Camillo J. Taylor
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the capabilities of foundation models in Visual Question Answering (VQA) tasks without fine-tuning on specific datasets. The authors propose an adaptive multi-agent system, Multi-Agent VQA, which leverages specialized agents to overcome limitations in object detection and counting. Unlike existing approaches, this study focuses on the system’s performance under zero-shot scenarios, making it more practical and robust in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models can answer questions about pictures without needing any special training. The researchers came up with a new way to use these models, called Multi-Agent VQA, which uses smaller “helper” agents to help the model understand what it’s looking at. This makes the model better at things like counting and finding objects in pictures. The study shows how well this system works without any special training, and highlights some areas where it can improve. |
Keywords
* Artificial intelligence * Fine tuning * Object detection * Question answering * Zero shot
