no code implementations • 4 Aug 2023 • Jiyong Moon, Junseok Lee, Yunju Lee, Seongsik Park
Therefore, we propose multi-scale patch selection (MSPS) to improve the multi-scale capabilities of existing ViT-based models.
1 code implementation • 30 May 2023 • Sungwon Kim, Junseok Lee, Namkyeong Lee, Wonjoong Kim, Seungyoon Choi, Chanyoung Park
To solve this problem, it is important for GNNs to be able to classify nodes with a limited number of labeled nodes, known as few-shot node classification.
1 code implementation • 29 Apr 2023 • Namkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim, Junseok Lee, Chanyoung Park
Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications.
1 code implementation • 28 Nov 2022 • Sein Kim, Namkyeong Lee, Junseok Lee, Dongmin Hyun, Chanyoung Park
In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data.
1 code implementation • 29 Sep 2022 • Sungho Shin, Joosoon Lee, Junseok Lee, Yeonguk Yu, Kyoobin Lee
Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images.
1 code implementation • 21 Aug 2022 • Namkyeong Lee, Dongmin Hyun, Junseok Lee, Chanyoung Park
Despite their success, existing GRL methods tend to overlook an inherent distinction between images and graphs, i. e., images are assumed to be independently and identically distributed, whereas graphs exhibit relational information among data instances, i. e., nodes.
2 code implementations • 4 Apr 2022 • Junseok Lee, Yunhak Oh, Yeonjun In, Namkyeong Lee, Dongmin Hyun, Chanyoung Park
Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i. e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes.
1 code implementation • 5 Dec 2021 • Namkyeong Lee, Junseok Lee, Chanyoung Park
Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods.
no code implementations • 22 Oct 2021 • Junseok Lee, Jongwon Kim, Jumi Park, Seunghyeok Back, Seongho Bak, Kyoobin Lee
This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network.
no code implementations • 22 Aug 2020 • Jongwon Kim, Sungho Shin, Yeonguk Yu, Junseok Lee, Kyoobin Lee
We divided a single deep learning architecture into a common extractor, a cloud model and a local classifier for the distributed learning.