Integrating Expert Knowledge into Adversarial Model for Enhanced Text-to-Image Synthesis

Text-to-image synthesis is an advanced computer vision task that aims to generate images given textual descriptions. While conventional methods have demonstrated an impressive capacity to render high-quality images, they frequently encounter challenges in the realm of producing fine-grained images that embody complex semantic details. To address this, we propose a novel that integrates Expert Knowledge into an Adversarial model (abbreviated as EKA). By utilizing the BLIP expert model to guide the discriminator, EKA encourages the generator to produce more realistic and fine-grained images with complex semantics. In addition, we design a semantic enhancement learning (SEL) module, which generates the fine-grained semantics feature representation by fusing features before and after affine transformations. To maintain semantic consistency between generated images and texts, we also introduce a semantic alignment loss. Extensive experiments on public benchmark datasets confirm the superiority of our approach.