RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering
Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B
Abstract
In this paper, we propose a method to improve the reasoning capabilities of Visual Question Answering (VQA) systems by integrating Dense Passage Retrievers (DPRs) with Vision Language Models (VLMs). While recent works focus on the application of knowledge graphs and chain-of-thought reasoning, we recognize that the complexity of graph neural networks and end-to-end training remain significant challenges. To address these issues, we introduce **R**elevance **G**uided **VQA** (**RG-VQA**), a retriever-generator pipeline that uses DPRs to efficiently extract relevant information from structured knowledge bases. Our approach ensures scalability to large graphs without significant computational overhead. Experiments on the ScienceQA dataset show that RG-VQA achieves state-of-the-art performance, surpassing human accuracy and outperforming GPT-4 by more than . This demonstrates the effectiveness of RG-VQA in boosting the reasoning capabilities of VQA systems and its potential for practical applications.
BibTeX
@inproceedings{emnlp2025_rgvqaleveragingr,
title = {RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering},
author = {Settaluri Lakshmi Sravanthi and Pulkit Agarwal and Debjyoti Mondal and Rituraj Singh and Subhadarshi Panda and Ankit Mishra and Kiran Pradeep and Srihari K B and Godawari Sudhakar Rao and Pushpak Bhattacharyya},
booktitle = {EMNLP 2025},
year = {2025}
}