Dayoung Gong

I am a graduate student in the Graduate School of Artificial Intelligence integrated M.S. & Ph.D. program at POSTECH. I am a member of the Computer Vision Lab at POSTECH, advised by Prof. Minsu Cho. Previously, I completed my B.S. in Convergence IT Engineering at POSTECH.

My research interests lie in computer vision and deep learning, especially in understanding temporal and semantic relations between actions in long-term videos. I’ve worked on long-term action anticipation. If you are interested in my research projects, please feel free to contact me by clicking one of the icons below.

News

Mar 3, 2022	📝 One paper is accepted to CVPR 2022.
Dec 1, 2020	I won the POSTECH Creative Self-Research Scholarship.

Education

Mar, 2020 - Present	Pohang University of Science and Technology (POSTECH), Pohang, South Korea M.S. & Ph.D. Student in Artificial Intelligence Advisor: Prof. Minsu Cho.
Mar, 2014 - Aug, 2019	Pohang University of Science and Technology (POSTECH), Pohang, South Korea B.S in Convergence IT Engineering

Publications

Future Transformer for Long-term Action Anticipation

Dayoung Gong, Joonseok Lee, Manjin Kim, Seong Jong Ha, and Minsu Cho

IEEE/CVF Conference on Computer Vision and Pattern Recogntiion (CVPR), 2022

Abs arXiv Code Website

The task of predicting future actions from a video is crucial for a real-world agent interacting with others. When anticipating actions in the distant future, we humans typically consider long-term relations over the whole sequence of actions, i.e., not only observed actions in the past but also potential actions in the future. In a similar spirit, we propose an end-to-end attention model for action anticipation, dubbed Future Transformer (FUTR), that leverages global attention over all input frames and output tokens to predict a minutes-long sequence of future actions. Unlike the previous autoregressive models, the proposed method learns to predict the whole sequence of future actions in parallel decoding, enabling more accurate and fast inference for long-term anticipation. We evaluate our method on two standard benchmarks for long-term action anticipation, Breakfast and 50 Salads, achieving state-of-the-art results.

Honors and Awards

POSTECH Creative Self-Research Scholarship, 2020 ($5,000)