Image Editing via Generative Models: A Survey

PhD Qualifying Examination


Title: "Image Editing via Generative Models: A Survey"

by

Mr. Jiapeng ZHU


Abstract:

Image editing has witnessed significant advancements with the advent of deep 
learning techniques, particularly generative models. In this paper, we 
present a comprehensive study of three popular generative models - 
Generative Adversarial Networks (GANs), diffusion models, and autoregressive 
models, and their applications in image editing tasks. We introduce the 
basics of these models and discuss their pros and cons. GANs excel in 
generating high-quality single-object images while maintaining a desirable 
continuous latent space. Diffusion models use step-by-step denoising to 
generate images, which have garnered significant interest, particularly in 
the domain of large-scale text-to-image models. Autoregressive models 
synthesize images of arbitrary sizes and resolutions pixel-by-pixel by 
modeling the conditional distribution of each pixel given its predecessors. 
We compare these models and discuss their strengths and weaknesses in 
different scenarios. We also talk about current challenges and future 
research in image editing with generative models. Our study aims to help 
researchers and practitioners understand and apply these models in image 
editing.


Date:                   Friday, 24 January 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Dr. Qifeng Chen (Supervisor)
                        Prof. Dit-Yan Yeung (Chairperson)
                        Dr. Dan Xu
                        Dr. Wenhan Luo (AMC)