Text to Image Synthesis based on Multi-Perspective Fusion

In this paper, we propose a multi-perspective fusion method to improve the performance of text-to-image synthesis. From the perspective of the generator, we introduce a dynamic selection method to make the text feature match the corresponding image feature better, while the multi-class discriminant method with mask segmentation image as the extra type is introduced from the perspective of the discriminator to improve its discrimination ability. Through the effective integration of these two aspects of improvement, more excellent results by our method are obtained. Experiments on the Caltech-UCSD Birds 200 (CUB) and Microsoft Common Objects in Context (MS COCO) datasets demonstrate our method's effectiveness and superiority. The qualitative and quantitative experiments validate that our method is superior to the existing state-of-theart methods.