Generative AI in Vision: A Survey on Models, Metrics and Applications
Gaurav Raut Apoorv Singh
Generative AI models have revolutionized various fields by enabling the
creation of realistic and diverse data samples. Among these models, diffusion
models have emerged as a powerful approach for generating high-quality images,
text, and audio. This survey paper provides a comprehensive overview of
generative AI diffusion and legacy models, focusing on their underlying
techniques, applications across different domains, and their challenges. We
delve into the theoretical foundations of diffusion models, including concepts
such as denoising diffusion probabilistic models (DDPM) and score-based
generative modeling. Furthermore, we explore the diverse applications of these
models in text-to-image, image inpainting, and image super-resolution, along
with others, showcasing their potential in creative tasks and data
augmentation. By synthesizing existing research and highlighting critical
advancements in this field, this survey aims to provide researchers and
practitioners with a comprehensive understanding of generative AI diffusion and
legacy models and inspire future innovations in this exciting area of
artificial intelligence.