Advances in Text-to-Image Generation: Integrating Transformer Models and Self-Attention Mechanisms - Yuan , Chang (Transactions on Computer Science and Intelligent Systems Research, 2024)
This study provides a comprehensive overview of advancements in Text-to-Image (TTI) generation through the application of Transformer models and Self-Attention Mechanisms. It begins with a review of the evolution of Generative Adversarial Networks (GANs) and highlights the benefits introduced by Self-Attention, such as improved contextual understanding and clearer image generation. The paper explores the theoretical foundations of Transformers and GANs, detailing how their integration can enhance TTI tasks. It also examines several leading models that employ these methodologies and presents quantitative performance evaluations comparing these models with other commonly used approaches. The findings indicate that Transformer-based modifications significantly improve TTI performance. The study concludes by assessing the current state of Self-Attention techniques and identifying potential research directions, such as exploring multi-head, hard, and soft attention mechanisms. These future research efforts are expected to further refine TTI capabilities and address existing challenges, providing deeper insights and more robust solutions for generating diverse and high-quality images from textual descriptions.