DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis (1904.01310v1)

Abstract: In this paper, we focus on generating realistic images from text descriptions. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. Most existing text-to-image synthesis methods have two main problems. (1) These methods depend heavily on the quality of the initial images. If the initial image is not well initialized, the following processes can hardly refine the image to a satisfactory quality. (2) Each word contributes a different level of importance when depicting different image contents, however, unchanged text representation is used in existing image refinement processes. In this paper, we propose the Dynamic Memory Generative Adversarial Network (DM-GAN) to generate high-quality images. The proposed method introduces a dynamic memory module to refine fuzzy image contents, when the initial images are not well generated. A memory writing gate is designed to select the important text information based on the initial image content, which enables our method to accurately generate images from the text description. We also utilize a response gate to adaptively fuse the information read from the memories and the image features. We evaluate the DM-GAN model on the Caltech-UCSD Birds 200 dataset and the Microsoft Common Objects in Context dataset. Experimental results demonstrate that our DM-GAN model performs favorably against the state-of-the-art approaches.

DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis

The paper presents Dynamic Memory Generative Adversarial Networks (DM-GAN), a novel approach designed to enhance text-to-image synthesis by addressing existing challenges in initial image quality and text representation. Traditional models generate an initial image that is subsequently refined; however, these often struggle if the initial image lacks detail or is poorly initialized. Moreover, most approaches use static text representations that do not account for the varying significance of words in relation to the image content.

Key Innovations

DM-GAN introduces several key mechanisms to address these issues:

1. Dynamic Memory Module: This component enhances image refinement by integrating a key-value memory structure. It processes both image queries and memory reads to yield more accurate refinements based on text and image features.
2. Memory Writing Gate: This gate selectively encodes pertinent text information into memory, dynamically aligning relevant words with the generated image content.
3. Response Gate: Used to intelligently blend memory-read data with image features, allowing more coherent integration and feature enhancement.

Methodology

DM-GAN operates in two main stages:

• Initial Image Generation: The preliminary stage produces a basic low-resolution image from textual input. It supplies initial image features which are then leveraged in subsequent refinement.
• Dynamic Memory-Based Refinement: This multi-step process iteratively refines the initial image using dynamic text representation. The memory module and gating mechanisms help in translating text into comprehensive and visually consistent images.

Experimental Evaluation

The DM-GAN model was rigorously tested on the Caltech-UCSD Birds 200 (CUB) and Microsoft COCO datasets, demonstrating superior performance over current state-of-the-art methods:

• Inception Score (IS): Achieved 4.75 on CUB, indicating higher visual quality and diversity.
• Fréchet Inception Distance (FID): Reduced to 16.09 on CUB, reflecting a closer approximation to actual image distributions compared to previous methods.
• R-Precision: Indicated improvement in image-text alignment with an increase on both datasets, spotlighting the effectiveness of the dynamic memory mechanism.

Implications and Future Work

The introduction of dynamic memory into GANs for text-to-image tasks signifies a significant advancement in generating coherent and photo-realistic imagery from descriptive text. By adaptively leveraging semantic relevance through advanced memory gating, DM-GAN addresses previous deficiencies in initial image accuracy and text interpretation.

The potential applications of DM-GAN extend into areas requiring high-fidelity image generation from complex textual inputs, such as automated art creation, advanced search engines, and enhanced virtual reality content generation.

Future research could further optimize initial generation stages, enhancing overall quality and utility through more intricate structural understanding, potentially leading to improved multi-object scene synthesis and layout management. As AI continues to evolve, approaches like DM-GAN could form the backbone of more refined, context-aware generative models.