Hyung-gun Chi

Hyung-gun Chi

Hyung-gun Chi is a PHD student in Electrical and Computer Engineering at Purdue University. Before he joining the C-Design Lab, He received his B.S degree from the school of Mechanical Engineering at Yonsei University, South Korea in 2017. His research interests are computer vision, and machine learning.
InfoGCN: Representation Learning for Human Skeleton-based Action Recognition

InfoGCN: Representation Learning for Human Skeleton-based Action Recognition

Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022

Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention. Although several studies have focused on...

First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset

First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset

Sangpil Kim, Hyung-gun Chi, Xiao Hu, Anirudh Vegesana, Karthik Ramani
In proceedings of the 31st British Machine Vision Conference (BMVC)

Abstract:  First-person-view videos of hands interacting with tools are widely used in the computer vision industry. However, creating a dataset with pixel-wise segmentation of hands is challenging since most videos are captured with fingertips...

A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks

A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks

Sangpil Kim*, Hyung-gun Chi*, Xiao Hu, Qixing Huang, Karthik Ramani
In proceedings of 16th European Conference on Computer Vision (ECCV)

We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named Mechanical Components Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components. The dataset enables data-driven...

Latent transformations neural network for object view synthesis

Latent transformations neural network for object view synthesis

Sangpil Kim, Nick Winovich, Hyung-Gun Chi, Guang Lin, Karthik Ramani
The Visual Computer (2019): 1-15.

We propose a fully convolutional conditional generative neural network, the latent transformation neural network, capable of rigid and non-rigid object view synthesis using a lightweight architecture suited for real-time applications and embedded...