A Simple Baseline for Knowledge-Based Visual Question Answering

• URL: http://arxiv.org/abs/2310.13570v2
• Date: Tue, 24 Oct 2023 13:24:25 GMT
• Title: A Simple Baseline for Knowledge-Based Visual Question Answering
• Authors: Alexandros Xenos, Themos Stafylakis, Ioannis Patras and Georgios Tzimiropoulos
• Abstract summary: This paper is on the problem of Knowledge-Based Visual Question Answering (KB-VQA) Our main contribution in this paper is to propose a much simpler and readily reproducible pipeline. Contrary to recent approaches, our method is training-free, does not require access to external databases or APIs, and achieves state-of-the-art accuracy on the OK-VQA and A-OK-VQA datasets.
• Score: 78.00758742784532
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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