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Visual Question Answering (VQA) is an intricate and demanding task that
integrates natural language processing (NLP) and computer vision (CV),
capturing the interest of researchers. The English language, renowned for its
wealth of resources, has witnessed notable advancements in both datasets and
models designed for VQA. However, there is a lack of models that target
specific countries such as Vietnam. To address this limitation, we introduce a
transformer-based Vietnamese model named BARTPhoBEiT. This model includes
pre-trained Sequence-to-Sequence and bidirectional encoder representation from
Image Transformers in Vietnamese and evaluates Vietnamese VQA datasets.
Experimental results demonstrate that our proposed model outperforms the strong
baseline and improves the state-of-the-art in six metrics: Accuracy, Precision,
Recall, F1-score, WUPS 0.0, and WUPS 0.9.
