  The output of text-to-image synthesis systems should be coherent, clear,
photo-realistic scenes with high semantic fidelity to their conditioned text
descriptions. Our Cross-Modal Contrastive Generative Adversarial Network
(XMC-GAN) addresses this challenge by maximizing the mutual information between
image and text. It does this via multiple contrastive losses which capture
inter-modality and intra-modality correspondences. XMC-GAN uses an attentional
self-modulation generator, which enforces strong text-image correspondence, and
a contrastive discriminator, which acts as a critic as well as a feature
encoder for contrastive learning. The quality of XMC-GAN's output is a major
step up from previous models, as we show on three challenging datasets. On
MS-COCO, not only does XMC-GAN improve state-of-the-art FID from 24.70 to 9.33,
but--more importantly--people prefer XMC-GAN by 77.3 for image quality and 74.1
for image-text alignment, compared to three other recent models. XMC-GAN also
generalizes to the challenging Localized Narratives dataset (which has longer,
more detailed descriptions), improving state-of-the-art FID from 48.70 to
14.12. Lastly, we train and evaluate XMC-GAN on the challenging Open Images
data, establishing a strong benchmark FID score of 26.91.
