Sentence guided object color change by adversarial learning.

In this paper, we propose a novel problem that is sentence guided object color change. Based on an original sentence and the corresponding image, by modifying this original sentence, the object color of corresponding image is changed along with the modified target sentence. This problem has two difficulties: (1) How to design a model to learn the change of sentence? (2) How to balance the relationship between the local object and the whole image during learning process? Confronted with these difficulties, as far as we know, few existing methods deal with them effectively. Therefore, we propose a new cascaded model to solve this problem based on generative adversarial networks (GANs). We employ the adversarial game to build a cascaded model, which learns the changed information of a sentence. Then, we specially design a penalty balance term to balance the relationship between local object and entire image in the generated image. Finally, experimental results on the flower and bird datasets demonstrate the validity of the proposed model. [ABSTRACT FROM AUTHOR]