envisioning answers: unleashing deep learning for visual question answering in artistic images

In specialized fields, the accurate answering of visual questions is crucial for practical applications, and this study focuses on improving a visual question-answering model for artistic images by utilizing a dataset with both visual and knowledge-based questions. the approach involves employing a pre-trained bert model to understand query nature and using the iqan model with mlb and mutan mechanisms for visual queries, along with an xlnet-based model for knowledge-based information. the results demonstrate a 78.92% accuracy for visual questions, 47.71% for knowledge-based questions, and an overall accuracy of 55.88% by combining both branches. additionally, the research explores the impact of parameters like the number of glances and activation functions on the model’s performance.