A Simple Baseline for Knowledge-Based Visual Question Answering

Alexandros Xenos, Themos Stafylakis, Ioannis Patras, Georgios Tzimiropoulos


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
Anthology ID:
2023.emnlp-main.919
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14871–14877
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2023.emnlp-main.919/
DOI:
10.18653/v1/2023.emnlp-main.919
Bibkey:
Cite (ACL):
Alexandros Xenos, Themos Stafylakis, Ioannis Patras, and Georgios Tzimiropoulos. 2023. A Simple Baseline for Knowledge-Based Visual Question Answering. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14871–14877, Singapore. Association for Computational Linguistics.
Cite (Informal):
A Simple Baseline for Knowledge-Based Visual Question Answering (Xenos et al., EMNLP 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2023.emnlp-main.919.pdf
Video:
 https://preview.aclanthology.org/build-pipeline-with-new-library/2023.emnlp-main.919.mp4