  Visual question answering (VQA) is the task of answering questions about an
image. The task assumes an understanding of both the image and the question to
provide a natural language answer. VQA has gained popularity in recent years
due to its potential applications in a wide range of fields, including
robotics, education, and healthcare. In this paper, we focus on
knowledge-augmented VQA, where answering the question requires commonsense
knowledge, world knowledge, and reasoning about ideas and concepts not present
in the image. We propose a multimodal framework that uses language guidance
(LG) in the form of rationales, image captions, scene graphs, etc to answer
questions more accurately. We benchmark our method on the multi-choice
question-answering task of the A-OKVQA, Science-QA, VSR, and IconQA datasets
using CLIP and BLIP models. We show that the use of language guidance is a
simple but powerful and effective strategy for visual question answering. Our
language guidance improves the performance of CLIP by 7.6% and BLIP-2 by 4.8%
in the challenging A-OKVQA dataset. We also observe consistent improvement in
performance on the Science-QA, VSR, and IconQA datasets when using the proposed
language guidances. The implementation of LG-VQA is publicly available at
https:// github.com/declare-lab/LG-VQA.
