Dual Adversarial Inference for Text-to-Image Synthesis

Qicheng Lao, Mohammad Havaei, Ahmad Pesaranghader, Francis Dutil, Lisa Di Jorio, Thomas Fevens; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 7567-7576

Abstract


Synthesizing images from a given text description involves engaging two types of information: the content, which includes information explicitly described in the text (e.g., color, composition, etc.), and the style, which is usually not well described in the text (e.g., location, quantity, size, etc.). However, in previous works, it is typically treated as a process of generating images only from the content, i.e., without considering learning meaningful style representations. In this paper, we aim to learn two variables that are disentangled in the latent space, representing content and style respectively. We achieve this by augmenting current text-to-image synthesis frameworks with a dual adversarial inference mechanism. Through extensive experiments, we show that our model learns, in an unsupervised manner, style representations corresponding to certain meaningful information present in the image that are not well described in the text. The new framework also improves the quality of synthesized images when evaluated on Oxford-102, CUB and COCO datasets.

Related Material


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[bibtex]
@InProceedings{Lao_2019_ICCV,
author = {Lao, Qicheng and Havaei, Mohammad and Pesaranghader, Ahmad and Dutil, Francis and Jorio, Lisa Di and Fevens, Thomas},
title = {Dual Adversarial Inference for Text-to-Image Synthesis},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}