Syntactic-Semantic Graph Fusion Generative Adversarial Network: SSGF-GAN

As AI technology continues to advance rapidly, more generative tasks have emerged, with text-to-image (T2I) generation becoming a popular area of research. Numerous studies have demonstrated the impressive capabilities that GANs bring to this field. In this paper, we focus on enhancing GANs by incorporating a novel component called the Syntactic-Semantic Graph Fusion Block, inspired by the Graph Neural Network (GNN). This block effectively captures the semantic and syntactic nuances of different sentences, leading to more detailed and refined image generation. Additionally, we introduce several other innovations to the framework, further boosting the model's performance. Experimental results on the CUB bird dataset demonstrate that our approach outperforms many current advanced models.