  The Generative Adversarial Network (GAN) has recently been applied to
generate synthetic images from text. Despite significant advances, most current
state-of-the-art algorithms are regular-grid region based; when attention is
used, it is mainly applied between individual regular-grid regions and a word.
These approaches are sufficient to generate images that contain a single object
in its foreground, such as a "bird" or "flower". However, natural languages
often involve complex foreground objects and the background may also constitute
a variable portion of the generated image. Therefore, the regular-grid based
image attention weights may not necessarily concentrate on the intended
foreground region(s), which in turn, results in an unnatural looking image.
Additionally, individual words such as "a", "blue" and "shirt" do not
necessarily provide a full visual context unless they are applied together. For
this reason, in our paper, we proposed a novel method in which we introduced an
additional set of attentions between true-grid regions and word phrases. The
true-grid region is derived using a set of auxiliary bounding boxes. These
auxiliary bounding boxes serve as superior location indicators to where the
alignment and attention should be drawn with the word phrases. Word phrases are
derived from analysing Part-of-Speech (POS) results. We perform experiments on
this novel network architecture using the Microsoft Common Objects in Context
(MSCOCO) dataset and the model generates $256 \times 256$ conditioned on a
short sentence description. Our proposed approach is capable of generating more
realistic images compared with the current state-of-the-art algorithms.
