Visual Question Answering (VQA) has recently emerged as a potential research domain, captivating the interest of many in the field of artificial intelligence and computer vision. Despite the prevalence of approaches in English, there is a notable lack of systems specifically developed for certain languages, particularly Vietnamese. This study aims to bridge this gap by conducting comprehensive experiments on the Vietnamese Visual Question Answering (ViVQA) dataset, demonstrating the effectiveness of our proposed model. In response to community interest, we have developed a model that enhances image representation capabilities, thereby improving overall performance in the ViVQA system. Specifically, our model integrates the Bootstrapping Language-Image Pre-training with frozen unimodal models (BLIP-2) and the convolutional neural network EfficientNet to extract and process both local and global features from images. This integration leverages the strengths of transformer-based architectures for capturing comprehensive contextual information and convolutional networks for detailed local features. By freezing the parameters of these pre-trained models, we significantly reduce the computational cost and training time, while maintaining high performance. This approach significantly improves image representation and enhances the performance of existing VQA systems. We then leverage a multi-modal fusion module based on a general-purpose multi-modal foundation model (BEiT-3) to fuse the information between visual and textual features. Our experimental findings demonstrate that our model surpasses competing baselines, achieving promising performance. This is particularly evident in its accuracy of $71.04\%$ on the test set of the ViVQA dataset, marking a significant advancement in our research area. The code is available at https://github.com/nngocson2002/ViVQA.

本研究针对越南视觉问答系统的缺乏问题，通过在越南视觉问答(ViVQA)数据集上进行全面实验，提出了一种新的模型。该模型结合了语言-图像预训练与卷积神经网络，有效提升了图像表示能力，显著提高了VQA系统的整体性能，并在ViVQA测试集上实现了71.04%的准确率，标志着这一领域的重要进展。

通过变换器和卷积集成推进越南视觉问答研究