RefineNet: Enhancing Text-to-Image Conversion with High-Resolution and
  Detail Accuracy through Hierarchical Transformers and Progressive Refinement
Fan Shi
In this research, we introduce RefineNet, a novel architecture designed to
address resolution limitations in text-to-image conversion systems. We explore
the challenges of generating high-resolution images from textual descriptions,
focusing on the trade-offs between detail accuracy and computational
efficiency. RefineNet leverages a hierarchical Transformer combined with
progressive and conditional refinement techniques, outperforming existing
models in producing detailed and high-quality images. Through extensive
experiments on diverse datasets, we demonstrate RefineNet's superiority in
clarity and resolution, particularly in complex image categories like animals,
plants, and human faces. Our work not only advances the field of image-to-text
conversion but also opens new avenues for high-fidelity image generation in
various applications.