Improving Text-to-Image Synthesis Using Contrastive Learning

TL;DR

This paper introduces a contrastive learning framework to improve the semantic consistency and quality of images generated from text descriptions, addressing caption variance issues in text-to-image synthesis.

Contribution

It proposes a novel contrastive learning method applied during pretraining and GAN training to enhance semantic alignment in text-to-image models.

Findings

Significant improvement in FID scores on COCO dataset

Enhanced semantic consistency in generated images

Better performance on quality metrics like IS and R-precision

Abstract

The goal of text-to-image synthesis is to generate a visually realistic image that matches a given text description. In practice, the captions annotated by humans for the same image have large variance in terms of contents and the choice of words. The linguistic discrepancy between the captions of the identical image leads to the synthetic images deviating from the ground truth. To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images. In the pretraining stage, we utilize the contrastive learning approach to learn the consistent textual representations for the captions corresponding to the same image. Furthermore, in the following stage of GAN training, we employ the contrastive learning method to enhance the consistency between the generated images from the captions related to the same image. We evaluate our approach over two popular text-to-image synthesis models, AttnGAN and DM-GAN, on datasets CUB and COCO, respectively. Experimental results have shown that our approach can effectively improve the quality of synthetic images in terms of three metrics: IS, FID and R-precision. Especially, on the challenging COCO dataset, our approach boosts the FID signifcantly by 29.60% over AttnGAN and by 21.96% over DM-GAN.