Hee-Yeon Choi  Dong-Wan Choi

10.5626/JOK.2025.52.1.62

visual question generation variational auto-encoder Multi Modal image-grounded text generation

Visual Question Generation (VQG) aims to generate questions based on a given image, often utilizing additional information such as answers or answer types if necessary. A VQG system should be able to generate diverse questions for a single image, while maintaining relevance to the image alongside its additional information. However, models that highly focus on relevance to the image might overfit to the dataset, leading to limited diversity, while those that emphasize diversity might generate questions less related to the input. Therefore, balancing these two aspects is crucial in VQG. To address this challenge, we proposed BCVQG (BLIP-CVAE VQG), a system that could integrate a pre-trained vision-language model with a Conditional Variational AutoEncoder (CVAE). The effectiveness of the proposed method was validated through quantitative and qualitative evaluations on the VQA2.0 dataset.