Instance Mask Embedding and Attribute-Adaptive Generative Adversarial Network for Text-to-Image Synthesis

Abstract

Existing image generation models have achieved the synthesis of reasonable individuals and complex but low-resolution images. Directly from complicated text to high-resolution image generation still remains a challenge. To this end, we propose the instance mask embedding and attribute-adaptive generative adversarial network (IMEAA-GAN). Firstly, we use the box regression network to compute a global layout containing the class labels and locations for each instance. Then the global generator encodes the layout, combines the whole text embedding and noise to preliminarily generate a low-resolution image; the instance embedding mechanism is used firstly to guide local refinement generators obtain fine-grained local features and generate a more realistic image. Finally, in order to synthesize the exact visual attributes, we introduce the multi-scale attribute-adaptive discriminator, which provides local refinement generators with the specific training signals to explicitly generate instance-level features. Extensive experiments based on the MS-COCO dataset and the Caltech-UCSD Birds-200-2011 dataset show that our model can obtain globally consistent attributes and generate complex images with local texture details.

Global-Affine and Local-Specific Generative Adversarial Network for semantic-guided image generation

“…Furthermore, considering the advantages of the multi-stage generation strategy [23], we specifically implement a global-affine and also a local-specific generator for image synthesis. In particular, the feature filtering mechanism proposed in the local-specific generator is employed to pay attention to all kinds of instances, especially the tiny ones, and learn the diverse and detailed features.…”