  We propose a novel lightweight generative adversarial network for efficient
image manipulation using natural language descriptions. To achieve this, a new
word-level discriminator is proposed, which provides the generator with
fine-grained training feedback at word-level, to facilitate training a
lightweight generator that has a small number of parameters, but can still
correctly focus on specific visual attributes of an image, and then edit them
without affecting other contents that are not described in the text.
Furthermore, thanks to the explicit training signal related to each word, the
discriminator can also be simplified to have a lightweight structure. Compared
with the state of the art, our method has a much smaller number of parameters,
but still achieves a competitive manipulation performance. Extensive
experimental results demonstrate that our method can better disentangle
different visual attributes, then correctly map them to corresponding semantic
words, and thus achieve a more accurate image modification using natural
language descriptions.
