Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation

This is the authors' abstract. We don't add key points for this paper.

We propose a novel lightweight generative adversarial network for efficient manipulation using natural language descriptions. To achieve this, a new-level discriminator is proposed, which provides the generator with-grained training feedback at word-level, to facilitate training a generator that has a small number of parameters, but can still focus on specific visual attributes of an image, and then edit them affecting other contents that are not described in the text., thanks to the explicit training signal related to each word, the can also be simplified to have a lightweight structure. Compared the state of the art, our method has a much smaller number of parameters, still achieves a competitive manipulation performance. Extensive results demonstrate that our method can better disentangle visual attributes, then correctly map them to corresponding semantic, and thus achieve a more accurate image modification using natural descriptions.