Multi-branch Semantic Learning Network for Text-to-Image Synthesis

In this paper, we propose a multi-branch semantic learning network (MSLN) to generate image according to textual description by taking into account global and local textual semantics, which consists of two stages. The first stage generates a coarse-grained image based on the sentence features. In the second stage, a multi-branch fine-grained generation model is constructed to inject the sentence-level and word-level semantics into two coarse-grained images by global and local attention modules, which generate global and local fine-grained image textures, respectively. In particular, we devise a channel fusion module (CFM) to fuse the global and local fine-grained features in the multi-branch fine-grained stage and generate the output image. Extensive experiments conducted on the CUB-200 dataset and Oxford-102 dataset demonstrate the superior performance of the proposed method. (e.g., FID is reduced from 16.09 to 14.43 on CUB-200).