  Typical methods for text-to-image synthesis seek to design effective
generative architecture to model the text-to-image mapping directly. It is
fairly arduous due to the cross-modality translation. In this paper we
circumvent this problem by focusing on parsing the content of both the input
text and the synthesized image thoroughly to model the text-to-image
consistency in the semantic level. Particularly, we design a memory structure
to parse the textual content by exploring semantic correspondence between each
word in the vocabulary to its various visual contexts across relevant images
during text encoding. Meanwhile, the synthesized image is parsed to learn its
semantics in an object-aware manner. Moreover, we customize a conditional
discriminator to model the fine-grained correlations between words and image
sub-regions to push for the text-image semantic alignment. Extensive
experiments on COCO dataset manifest that our model advances the
state-of-the-art performance significantly (from 35.69 to 52.73 in Inception
Score).
