Jeongsoo Park
jespark at umich dot edu

I am a second-year CSE PhD student at the University of Michigan, advised by Prof. Andrew Owens. I have a general interest in computer vision, image processing, and visual forensics.

I graduated with a master's degree in ECE from the University of Michigan, during which I was advised by Prof. Justin Johnson.

I received a BS degree in Electronic and Electrical Engineering from Sungkyunkwan University, where I was advised by Prof. Jong Hwan Ko.

LinkedIn  /  Google Scholar  /  Twitter  /  Github

profile photo
Research

I have been working on an intersection of computer vision, image processing, and image forensics.

In my recent work Community Forensics, I study the generalization of fake image detectors by training them using images sampled from thousands of generators. In RGB no more, I train Vision Transformers directly from the encoded features of a JPEG image to accelerate the entire pipeline. In Auto-Tiler, I design an efficient compression scheme for over-the-network model inference.

Will be updated! Community Forensics: Using Thousands of Generators to Train Fake Image Detectors
Jeongsoo Park, Andrew Owens
arXiv 2024
Project page / arXiv

We train fake image detectors using images from thousands of various generators to study generalization in detecting generated images.

RGB no more: Minimally-decoded JPEG Vision Transformers
Jeongsoo Park, Justin Johnson
CVPR 2023
Project page / Code / Paper

We train ViTs directly from JPEG encoded features and accelerate train and eval by up to 39.2% and 17.9%.

Auto-Tiler: Variable-Dimension Autoencoder with Tiling for Compressing Intermediate Feature Space of Deep Neural Networks for Internet of Things
Jeongsoo Park, Jungrae Kim, Jong Hwan Ko
Sensors, 2021
Paper

We use autoencoders and show up to 67% higher accuracy and 81% reduced latency during network-constrained inference versus the image or video codec-based compression.

This website uses the html template by Jon Barron.