MirrorGAN: Learning Text-to-image Generation by Redescription (1903.05854v1)

Abstract: Generating an image from a given text description has two goals: visual realism and semantic consistency. Although significant progress has been made in generating high-quality and visually realistic images using generative adversarial networks, guaranteeing semantic consistency between the text description and visual content remains very challenging. In this paper, we address this problem by proposing a novel global-local attentive and semantic-preserving text-to-image-to-text framework called MirrorGAN. MirrorGAN exploits the idea of learning text-to-image generation by redescription and consists of three modules: a semantic text embedding module (STEM), a global-local collaborative attentive module for cascaded image generation (GLAM), and a semantic text regeneration and alignment module (STREAM). STEM generates word- and sentence-level embeddings. GLAM has a cascaded architecture for generating target images from coarse to fine scales, leveraging both local word attention and global sentence attention to progressively enhance the diversity and semantic consistency of the generated images. STREAM seeks to regenerate the text description from the generated image, which semantically aligns with the given text description. Thorough experiments on two public benchmark datasets demonstrate the superiority of MirrorGAN over other representative state-of-the-art methods.

Analysis of MirrorGAN: Learning Text-to-Image Generation by Redescription

The paper "MirrorGAN: Learning Text-to-image Generation by Redescription" presents a novel approach to the problem of generating images from textual descriptions, focusing on improving both visual realism and semantic consistency. Despite advancements with generative adversarial networks (GANs), ensuring that generated images align semantically with their descriptive text remains a complex challenge. This paper proposes a framework called MirrorGAN that utilizes a text-to-image-to-text architecture to address this issue effectively.

Framework Overview

MirrorGAN introduces a three-module architecture:

1. Semantic Text Embedding Module (STEM): This module creates word- and sentence-level embeddings from text descriptions. It utilizes recurrent neural networks to capture the semantic essence necessary for guiding the image generation process.
2. Global-Local Collaborative Attentive Module (GLAM): GLAM functions within a cascaded image generation setup to produce images from coarse to fine scales. It employs both local word attention and global sentence attention, thereby maintaining semantic consistency and enhancing the generated images' diversity.
3. Semantic Text Regeneration and Alignment Module (STREAM): This module regenerates text descriptions from the images produced, ensuring semantic alignment with the original input text. By reconstructing the input text, MirrorGAN effectively mirrors the initial semantic content.

Key Methodological Advancements

• Cascaded Architecture: MirrorGAN employs a multi-stage process to progressively refine images, which leads to enhanced detail and semantic alignment. Each stage leverages the attention mechanisms to guide improvements from the previous resolution level.
• Comprehensive Loss Functions: The system is trained with adversarial losses for visual realism and semantic consistency. Furthermore, a cross-entropy-based text-semantics reconstruction loss is employed to ensure cross-modal alignment between the text and image outputs.

Empirical Findings

The paper reports performance evaluations over two benchmark datasets, CUB and COCO, showcasing MirrorGAN's superiority over established methods like AttnGAN and StackGAN++. The results highlight:

• An increase in both Inception Scores and R-precision scores, suggesting improved image quality and semantic correspondence.
• Enhanced human perception paper outcomes, which indicate that viewers find MirrorGAN's images more realistic and semantically congruent with descriptions.

Theoretical and Practical Implications

MirrorGAN's novel integration of T2I and I2T tasks into a unified framework represents a significant conceptual advancement, suggesting potential pathways for cross-modal learning applications. Practically, the implications for domains requiring high fidelity in data representation, such as autonomous vehicles and interactive media, are noteworthy. This framework could lead to more intuitive and semantically aware generation systems.

Future Directions

Future research may explore optimizing the training of all modules in an end-to-end manner to further enhance performance and efficiency. Additionally, leveraging more advanced text embedding and image captioning techniques could bolster MirrorGAN’s capabilities. Exploring the synergy between this approach and other cross-modal algorithms like CycleGAN could further expand its application scope.

In conclusion, MirrorGAN successfully demonstrates a method to improve the alignment of generated images with their descriptive text, setting a benchmark in the fusion of GAN-based image generation and semantic understanding. This contributes significantly to the ongoing exploration of multi-modal deep learning systems.