Cross-Modal Contrastive Learning for Text-to-Image Generation
This addresses the challenge of text-to-image generation for applications requiring high semantic fidelity, representing a strong specific gain rather than an incremental improvement.
The paper tackles the problem of generating coherent and photo-realistic images from text descriptions by proposing XMC-GAN, which improves state-of-the-art FID scores from 24.70 to 9.33 on MS-COCO and from 48.70 to 14.12 on Localized Narratives, with human preferences of 77.3% for image quality and 74.1% for alignment.
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.