LFR-GAN: Local Feature Refinement based Generative Adversarial Network for Text-to-Image Generation

Abstract

Text-to-image generation aims to generate images from text descriptions. Its main challenge lies in two aspects: (1) Semantic consistency, i.e., the generated images should be semantically consistent with the input text; and (2) Visual reality, i.e., the generated images should look like real images. To ensure text-image consistency, existing works mainly learn to establish the cross-modal representations via a text encoder and image encoder. However, due to the limited representation capability of the fixed-length embeddings and the flexibility of the free-form text descriptions, the learned text-to-image model is incapable of maintaining the semantic consistency between image local regions and fine-grained descriptions. As a result, the generated images sometimes miss some fine-grained attributes of the generated object, such as the color or shape of a part of the object. To address this issue, this paper proposes a Local Feature Refinement Based Generative Adversarial Network (LFR-GAN) , which first divides the text into some independent fine-grained attributes and generates an initial image, then refines the image details based on these attributes. The main contributions are three-fold: (1) An attribute modeling approach is proposed to model the fine-grained text descriptions by mapping them into representations of independent attributes, which provides more fine-grained details for image generation. (2) A local feature refinement approach is proposed to enable the generated image to form a complete reflection of the fine-grained attributes contained in the text description. (3) A multi-stage generation approach is proposed to realize the fine-grained manipulation of complex images progressively, which aims to improve the performance of the refinement and generate photo-realistic images. Extensive experiments on the CUB and Oxford102 datasets show the effectiveness of our LFR-GAN approach in both text-to-image generation and text-guided image manipulation tasks. Our LFR-GAN approach shows superior performance compared to the state-of-the-art methods. The codes will be released at https://github.com/PKU-ICST-MIPL/LFR-GAN_TOMM2023 .