  As a challenging task, text-to-image generation aims to generate
photo-realistic and semantically consistent images according to the given text
descriptions. Existing methods mainly extract the text information from only
one sentence to represent an image and the text representation effects the
quality of the generated image well. However, directly utilizing the limited
information in one sentence misses some key attribute descriptions, which are
the crucial factors to describe an image accurately. To alleviate the above
problem, we propose an effective text representation method with the
complements of attribute information. Firstly, we construct an attribute memory
to jointly control the text-to-image generation with sentence input. Secondly,
we explore two update mechanisms, sample-aware and sample-joint mechanisms, to
dynamically optimize a generalized attribute memory. Furthermore, we design an
attribute-sentence-joint conditional generator learning scheme to align the
feature embeddings among multiple representations, which promotes the
cross-modal network training. Experimental results illustrate that the proposed
method obtains substantial performance improvements on both the CUB (FID from
14.81 to 8.57) and COCO (FID from 21.42 to 12.39) datasets.
