Text-to-Image Synthesis with Threshold-Equipped Matching-Aware GAN

In this paper, we propose a novel Equipped with Threshold Matching-Aware Generative Adversarial Network (ETMA-GAN) for text-to-image synthesis. By filtering inaccurate negative samples, the discriminator can more accurately determine whether the generator has generated the images correctly according to the descriptions. In addition, to enhance the discriminative model’s ability to discriminate and capture key semantic information, a word fine-grained supervisor is constructed, which in turn drives the generative model to achieve high-quality image detail synthesis. Numerous experiments and ablation studies on Caltech-UCSD Birds 200 (CUB) and Microsoft Common Objects in Context (MS COCO) datasets demonstrate the effectiveness and superiority of the proposed method over existing methods. In terms of subjective and objective evaluations, the model presented in this study has more advantages than the recently available state-of-the-art methods, especially regarding synthetic images with a higher degree of realism and better conformity to text descriptions.