ReasonVQA: 一个带有结构知识的多跳推理问答基准数据集
ReasonVQA: A Multi-hop Reasoning Benchmark with Structural Knowledge for Visual Question Answering
AI总结：
ReasonVQA是一个结合结构知识的多跳推理问答基准数据集，通过生成复杂问题挑战现有VQA模型，推动领域发展。
摘要：
本文提出了一个新的数据集ReasonVQA，用于视觉问答（VQA）任务。我们的数据集自动整合了结构化的百科知识，并通过低成本框架构建，能够生成复杂的多跳问题。我们在ReasonVQA上评估了最先进的VQA模型，实验证明ReasonVQA对这些模型提出了重大挑战，突显了其在基准测试和推动VQA领域发展的潜力。此外，我们的数据集可以轻松扩展输入图像；当前版本比需要外部知识的最大现有数据集大了一个数量级。
英文摘要：
In this paper, we propose a new dataset, ReasonVQA, for the Visual Question Answering (VQA) task. Our dataset is automatically integrated with structured encyclopedic knowledge and constructed using a low-cost framework, which is capable of generating complex, multi-hop questions. We evaluated state-of-the-art VQA models on ReasonVQA, and the empirical results demonstrate that ReasonVQA poses significant challenges to these models, highlighting its potential for benchmarking and advancing the field of VQA. Additionally, our dataset can be easily scaled with respect to input images; the current version surpasses the largest existing datasets requiring external knowledge by more than an order of magnitude.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
