Integrating features and harnessing pre-trained visual-language models for enhancing VQA reading comprehension

The Visual Question Answering (VQA) problem represents the fusion of natural language understanding (NLU) and computer vision, aiming to comprehend both textual queries and visual content. Recently, researchers have been focusing on the reading comprehension abilities of VQA models, specifically on their capacity to utilize information from scene texts to gather additional context for answering the posed questions. In this study, we present a fundamental approach for integrating diverse information contained in both images and questions. By leveraging a Transformer model, the proposed solution effectively addresses the VQA problem. Our approach, achieving the second position in the VLSP 2023 challenge on Visual Reading Comprehension for Vietnamese, highlights the effectiveness of our proposed method. This study contributes to the ongoing discourse on refining VQA models and emphasizes the potential for further advancements in this domain. The codes are available in the GitHub repository, i.e., https://github.com /truong-xuan-linh/FSO-implement.