Text to Image Generation with Conformer-GAN

Text-to-image generation (T2I) has been a popular research field in recent years, and its goal is to generate corresponding photorealistic images through natural language text descriptions. Existing T2I models are mostly based on generative adversarial networks, but it is still very challenging to guarantee the semantic consistency between a given textual description and generated natural images. To address this problem, we propose a concise and practical novel framework, Conformer-GAN. Specifically, we propose the Conformer block, consisting of the Convolutional Neural Network (CNN) and Transformer branches. The CNN branch is used to generate images conditionally from noise. The Transformer branch continuously focuses on the relevant words in natural language descriptions and fuses the sentence and word information to guide the CNN branch for image generation. Our approach can better merge global and local representations to improve the semantic consistency between textual information and synthetic images. Importantly, our Conformer-GAN can generate natural and realistic 512 × 512 images. Extensive experiments on the challenging public benchmark datasets CUB bird and COCO demonstrate that our method outperforms recent state-of-the-art methods both in terms of generated image quality and text-image semantic consistency.