Adversarial text-to-image synthesis: A review.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Text-to-image synthesis using generative adversarial networks (GANs) has advanced significantly in visual realism and semantic alignment.
• Despite progress, challenges remain in generating high-resolution images with multiple objects and developing reliable evaluation metrics.

Purpose of the Study:

• To review the state-of-the-art in adversarial text-to-image synthesis models.
• To propose a taxonomy based on supervision levels and critically examine current evaluation strategies.
• To identify future research directions for improving text-to-image generation.

Main Methods:

• Contextualizing the development of adversarial text-to-image synthesis models over the past five years.
• Proposing a taxonomy for these models based on their level of supervision.
• Critically analyzing existing evaluation metrics and methodologies.

Main Results:

• The review provides a comprehensive overview of adversarial text-to-image synthesis.
• Identified shortcomings in current evaluation metrics and highlighted areas for improvement.
• Proposed new research directions including better datasets, metrics, architectural designs, and training strategies.

Conclusions:

• Adversarial text-to-image synthesis is a rapidly evolving field with ongoing challenges.
• Further research is needed in high-resolution generation, multi-object synthesis, and robust evaluation.
• This review aims to guide researchers in advancing the field of text-to-image generation.