Leveraging Visual Question Answering to Improve Text-to-Image Synthesis

• URL: http://arxiv.org/abs/2010.14953v1
• Date: Wed, 28 Oct 2020 13:11:34 GMT
• Title: Leveraging Visual Question Answering to Improve Text-to-Image Synthesis
• Authors: Stanislav Frolov, Shailza Jolly, J\"orn Hees, Andreas Dengel
• Abstract summary: We propose an effective way to combine Text-to-Image (T2I) synthesis with Visual Question Answering (VQA) to improve the image quality and image-text alignment. We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal. Our method lowers the FID from 27.84 to 25.38 and increases the R-prec. from 83.82% to 84.79% when compared to the baseline.
• Score: 5.4897944234841445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating images from textual descriptions has recently attracted a lot of interest. While current models can generate photo-realistic images of individual objects such as birds and human faces, synthesising images with multiple objects is still very difficult. In this paper, we propose an effective way to combine Text-to-Image (T2I) synthesis with Visual Question Answering (VQA) to improve the image quality and image-text alignment of generated images by leveraging the VQA 2.0 dataset. We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal. We encourage images generated from QA pairs to look realistic and additionally minimize an external VQA loss. Our method lowers the FID from 27.84 to 25.38 and increases the R-prec. from 83.82% to 84.79% when compared to the baseline, which indicates that T2I synthesis can successfully be improved using a standard VQA model.

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