  Most existing text-to-image generation methods adopt a multi-stage modular
architecture which has three significant problems: 1) Training multiple
networks increases the run time and affects the convergence and stability of
the generative model; 2) These approaches ignore the quality of early-stage
generator images; 3) Many discriminators need to be trained. To this end, we
propose the Dual Attention Generative Adversarial Network (DTGAN) which can
synthesize high-quality and semantically consistent images only employing a
single generator/discriminator pair. The proposed model introduces
channel-aware and pixel-aware attention modules that can guide the generator to
focus on text-relevant channels and pixels based on the global sentence vector
and to fine-tune original feature maps using attention weights. Also,
Conditional Adaptive Instance-Layer Normalization (CAdaILN) is presented to
help our attention modules flexibly control the amount of change in shape and
texture by the input natural-language description. Furthermore, a new type of
visual loss is utilized to enhance the image resolution by ensuring vivid shape
and perceptually uniform color distributions of generated images. Experimental
results on benchmark datasets demonstrate the superiority of our proposed
method compared to the state-of-the-art models with a multi-stage framework.
Visualization of the attention maps shows that the channel-aware attention
module is able to localize the discriminative regions, while the pixel-aware
attention module has the ability to capture the globally visual contents for
the generation of an image.
