  Altering the content of an image with photo editing tools is a tedious task
for an inexperienced user. Especially, when modifying the visual attributes of
a specific object in an image without affecting other constituents such as
background etc. To simplify the process of image manipulation and to provide
more control to users, it is better to utilize a simpler interface like natural
language. Therefore, in this paper, we address the challenge of manipulating
images using natural language description. We propose the Two-sidEd Attentive
conditional Generative Adversarial Network (TEA-cGAN) to generate semantically
manipulated images while preserving other contents such as background intact.
TEA-cGAN uses fine-grained attention both in the generator and discriminator of
Generative Adversarial Network (GAN) based framework at different scales.
Experimental results show that TEA-cGAN which generates 128x128 and 256x256
resolution images outperforms existing methods on CUB and Oxford-102 datasets
both quantitatively and qualitatively.
