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.

本文研究了基于视觉问答（VQA）任务中基础模型的零 - shot 能力。我们提出了一种自适应多智能体系统，称为多智能体 VQA，通过使用专门的智能体作为工具，克服了基础模型在目标检测和计数方面的局限性。与现有方法不同的是，我们的研究重点在于系统在特定 VQA 数据集上不进行微调的性能，使其在开放世界中更实用和稳健。我们呈现了零 - shot 场景下的初步实验结果，并突出了一些失败案例，为未来的研究提供了新的方向。