基于知识的视觉问答的一个简单基线
A Simple Baseline for Knowledge-Based Visual Question Answering
AI总结：
本文研究基于知识的视觉问答问题，提出一种无需训练且不依赖外部数据库或API的简单流程，通过将包含问题信息的描述作为上下文提示LLaMA进行上下文学习，在OK-VQA和A-OKVQA数据集上实现了最先进的准确率。
摘要：
本文研究基于知识的视觉问答（KB-VQA）问题。近期工作强调，结合显式（通过外部数据库）和隐式（通过LLMs）知识对于有效回答需要外部知识的问题具有重要意义。此类方法的一个常见局限在于，它们由相对复杂的流程构成，且往往严重依赖访问GPT-3 API。本文的主要贡献是提出一种更简单且易于复现的流程，简而言之，该流程基于通过将包含问题信息的描述作为上下文信息来提示LLaMA（1和2），从而进行高效的上下文学习。与近期方法不同，我们的方法无需训练，不需要访问外部数据库或API，却在OK-VQA和A-OKVQA数据集上实现了最先进的准确率。最后，我们进行了几项消融研究以理解我们方法的重要方面。我们的代码已在 https://github.com/alexandrosXe/ASimple-Baseline-For-Knowledge-Based-VQA 上公开。
英文摘要：
This paper is on the problem of Knowledge-Based Visual Question Answering (KB-VQA). Recent works have emphasized the significance of incorporating both explicit (through external databases) and implicit (through LLMs) knowledge to answer questions requiring external knowledge effectively. A common limitation of such approaches is that they consist of relatively complicated pipelines and often heavily rely on accessing GPT-3 API. Our main contribution in this paper is to propose a much simpler and readily reproducible pipeline which, in a nutshell, is based on efficient in-context learning by prompting LLaMA (1 and 2) using question-informative captions as contextual information. Contrary to recent approaches, our method is training-free, does not require access to external databases or APIs, and yet achieves state-of-the-art accuracy on the OK-VQA and A-OK-VQA datasets. Finally, we perform several ablation studies to understand important aspects of our method. Our code is publicly available at https://github.com/alexandrosXe/ASimple-Baseline-For-Knowledge-Based-VQA
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。
