CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis

TL;DR

CPGAN introduces a content-parsing approach with a memory structure and object-aware semantics to improve text-to-image synthesis, achieving state-of-the-art results on COCO by focusing on semantic consistency.

Contribution

The paper proposes a novel content-parsing GAN framework that models semantic correspondence between text and images at a detailed level, enhancing text-image alignment.

Findings

Significant improvement in Inception Score from 35.69 to 52.73.

Effective modeling of semantic correspondence improves synthesis quality.

Outperforms existing methods on COCO dataset.

Abstract

Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly. It is fairly arduous due to the cross-modality translation. In this paper we circumvent this problem by focusing on parsing the content of both the input text and the synthesized image thoroughly to model the text-to-image consistency in the semantic level. Particularly, we design a memory structure to parse the textual content by exploring semantic correspondence between each word in the vocabulary to its various visual contexts across relevant images during text encoding. Meanwhile, the synthesized image is parsed to learn its semantics in an object-aware manner. Moreover, we customize a conditional discriminator to model the fine-grained correlations between words and image sub-regions to push for the text-image semantic alignment. Extensive experiments on COCO dataset manifest that our model advances the state-of-the-art performance significantly (from 35.69 to 52.73 in Inception Score).