no code implementations • 24 Jun 2024 • Kwang-Hyun Uhm, Seung-Won Jung, Sung-Hoo Hong, Sung-Jea Ko
We also present a multi-scale attention scheme to capture and aggregate temporal patterns of lesion features at different spatial scales for further improvement.
no code implementations • 9 Dec 2023 • Kwang-Hyun Uhm, Seung-Won Jung, Moon Hyung Choi, Sung-Hoo Hong, Sung-Jea Ko
In this paper, we propose a unified framework for kidney cancer diagnosis with incomplete multi-phase CT, which simultaneously recovers missing CT images and classifies cancer subtypes using the completed set of images.
no code implementations • 9 Dec 2023 • Kwang-Hyun Uhm, Hyunjun Cho, Zhixin Xu, Seohoon Lim, Seung-Won Jung, Sung-Hoo Hong, Sung-Jea Ko
In 2023, it is estimated that 81, 800 kidney cancer cases will be newly diagnosed, and 14, 890 people will die from this cancer in the United States.
no code implementations • 26 Jan 2022 • Yoon-Jae Yeo, Min-Cheol Sagong, Seung Park, Sung-Jea Ko, Yong-Goo Shin
Region-adaptive normalization (RAN) methods have been widely used in the generative adversarial network (GAN)-based image-to-image translation technique.
1 code implementation • CVPR 2022 • Seo-won Ji, Jeongmin Lee, Seung-Wook Kim, Jun-Pyo Hong, Seung-Jin Baek, Seung-Won Jung, Sung-Jea Ko
Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images.
4 code implementations • ICCV 2021 • Sung-Jin Cho, Seo-won Ji, Jun-Pyo Hong, Seung-Won Jung, Sung-Jea Ko
Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks.
Ranked #10 on
Deblurring
on RSBlur
no code implementations • 28 Jul 2021 • Min-Cheol Sagong, Yoon-Jae Yeo, Seung-Won Jung, Sung-Jea Ko
In addition, we propose an improved information aggregation module with PAKA, called the hierarchical PAKA module (HPM).
1 code implementation • 20 Nov 2019 • Kwang-Hyun Uhm, Seung-Wook Kim, Seo-won Ji, Sung-Jin Cho, Jun-Pyo Hong, Sung-Jea Ko
Recent research on learning a mapping between raw Bayer images and RGB images has progressed with the development of deep convolutional neural networks.
no code implementations • 19 Nov 2019 • Yong-Goo Shin, Yoon-Jae Yeo, Sung-Jea Ko
In adversarial learning, discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or non-robust features.
no code implementations • 18 Nov 2019 • Cheol-hwan Yoo, Seo-won Ji, Yong-Goo Shin, Seung-Wook Kim, Sung-Jea Ko
In this paper, we propose a hierarchically-structured convolutional recurrent neural network (HCRNN) with six branches that estimate the 3D position of the palm and five fingers independently.
no code implementations • 8 Nov 2019 • Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Ales Leonardis, Bolun Zheng, Xin Ye, Xiang Tian, Yaowu Chen, Xi Cheng, Zhen-Yong Fu, Jian Yang, Ming Hong, Wenying Lin, Wenjin Yang, Yanyun Qu, Hong-Kyu Shin, Joon-Yeon Kim, Sung-Jea Ko, Hang Dong, Yu Guo, Jie Wang, Xuan Ding, Zongyan Han, Sourya Dipta Das, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
no code implementations • 3 Jun 2019 • Min-Cheol Sagong, Yong-Goo Shin, Yoon-Jae Yeo, Seung Park, Sung-Jea Ko
Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator.
no code implementations • 22 May 2019 • Yong-Goo Shin, Min-Cheol Sagong, Yoon-Jae Yeo, Seung-Wook Kim, Sung-Jea Ko
To address this problem, we propose a novel network architecture called PEPSI: parallel extended-decoder path for semantic inpainting network, which aims at reducing the hardware costs and improving the inpainting performance.
no code implementations • 15 May 2019 • Yong-Goo Shin, Seung Park, Yoon-Jae Yeo, Min-Jae Yoo, Sung-Jea Ko
In the proposed method, the power consumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN).
2 code implementations • ECCV 2018 • Seung-Wook Kim, Hyong-Keun Kook, Jee-Young Sun, Mun-Cheon Kang, Sung-Jea Ko
To overcome this limitation, we propose a CNN-based object detection architecture, referred to as a parallel feature pyramid (FP) network (PFPNet), where the FP is constructed by widening the network width instead of increasing the network depth.