1 code implementation • 31 Mar 2022 • Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, Phillip Isola
The surprising effectiveness of visual prompting provides a new perspective on adapting pre-trained models in vision.
1 code implementation • CVPR 2020 • Hyojin Bahng, Sunghyo Chung, Seungjoo Yoo, Jaegul Choo
Despite remarkable success in unpaired image-to-image translation, existing systems still require a large amount of labeled images.
1 code implementation • 29 Nov 2019 • Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Kihwan Kim, Seungmin Jin, Sungahn Ko, Jaegul Choo
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences.
3 code implementations • ICML 2020 • Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh
This tactic is feasible in many scenarios where it is much easier to define a set of biased representations than to define and quantify bias.
1 code implementation • 9 Jun 2019 • Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung, Junsoo Lee, Jaehyuk Chang, Jaegul Choo
Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning.
no code implementations • 7 May 2018 • David Keetae Park, Seungjoo Yoo, Hyojin Bahng, Jaegul Choo, Noseong Park
Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images.
1 code implementation • ECCV 2018 • Hyojin Bahng, Seungjoo Yoo, Wonwoong Cho, David K. Park, Ziming Wu, Xiaojuan Ma, Jaegul Choo
This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette.