Abstract
In recent years, text-to-image synthesis techniques have made considerable breakthroughs, but the progress is restricted to simple scenes. Such techniques turn out to be ineffective if the text appears complex and contains multiple objects. To address this challenging issue, we propose a novel text-to-image synthesis model called Object-driven Self-Attention Generative Adversarial Network (Obj-SA-GAN), where self-attention mechanisms are utilised to analyse the information with different granularities at different stages, achieving full exploitation of text semantic information from coarse to fine. Complex datasets are used to evaluate the performance of the proposed model. The experimental results explicitly show that our model outperforms the state-of-the-art methods. This is because the proposed Obj-SA-GAN model utilises textual information, which provides a better understanding of complex scenarios.
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Li, R., Li, W., Yang, Y., Bai, Q. (2022). Obj-SA-GAN: Object-Driven Text-to-Image Synthesis with Self-Attention Based Full Semantic Information Mining. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_25
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