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Research Interests
Deep learning network architecture
Transfer learning
Vision Language Model
Medical image recognition
Industrial application with deep learning
Depth estimation, face recognition, and visual tracking
Deep learning network architecture
Enriching Local Patterns with Multi-Token Attention for Broad-Sight Neural Networks
Hankyul Kang, Jongbin ryu
Winter Conference on Applications of Computer Vision (WACV), 2025.
Channel Propagation Networks for Refreshable Vision Transformer
Junhyeong Go, Jongbin ryu
Winter Conference on Applications of Computer Vision (WACV), 2025.
Spatial Bias for Attention-free Non-local Neural Networks
Junhyung Ko and Jongbin Ryu
Expert Systems with Applications, 2024
Gramian Attention Heads are Strong yet Efficient Vision Learners
Jongbin Ryu, Dongyoon Han, and Jongwoo Lim,
International Conference on Computer Vision (ICCV), 2023
DFT-based Transformation Invariant Pooling Layer for Visual Classification
Jongbin Ryu, Ming-Hsuan Yang, and Jongwoo Lim
European Conference on Computer Vision (ECCV), 2018.
Medical image recognition
Generative Self-Supervised Learning for Medical Image Classification
Inhyuk Park, SungEun Kim and Jongbin Ryu
Asian Conference on Computer Vision (ACCV), 2024.
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
CVAMD participants including Jongbin Ryu
Medical Image Analysis (MedIA), 2024
Style-KD : Class-Imbalanced Medical Image Classification via Style Knowledge Distillation
Inhyuk Park, Wonhwa Kim, and Jongbin Ryu
Biomedical Signal Processing and Control, 2024
Attentional decoder networks for chest X-ray image recognition on high-resolution features
Hankyul Kang, Namkug Kim and Jongbin Ryu
Computer Methods and Programs in Biomedicine, 2024
Fine-Grained Self-Supervised Learning with Jigsaw puzzles for medical image classification
Wongi Park, Jongbin Ryu
Computers in Biology and Medicine, 2024.
Analyzing to discover origins of CNNs and ViT architectures in medical images
Seungmin Oh, Namkug Kim and Jongbin Ryu
Scientific Reports, 2024
Deep learning using computed tomography to identify high-risk patients for acute small bowel obstruction: development and validation of a prediction model
Seungmin Oh, Jongbin Ryu*, Ho-Jung Shin*, Jeong Ho Song, Sang-Yong, Son, Hoon Hur, Sang-Uk Han
International Journal of Surgery, 2023 (IF 15.3, JCR Top 1%)
Robust Asymmetric Loss for Multi-Label Long-Tailed Learning
Wonki Park, Inhyuk Park, SungEun and Jongbin Ryu
CVAMD workshop at International Conference on Computer Vision (ICCV), 2023
Deep learning algorithms for detecting and visualising intussusception on plain abdominal radiography in children: a retrospective multicenter study
Gitaek Kwon*, Jongbin Ryu*, Jaehoon Oh, Jongwoo Lim, Bo-kyeong Kang, Chiwon Ahn, Junwon Bae, and Dong Keon Lee. (* equal contribution)
Scientific reports 2020.
Learning algorithm for deep neural networks
Neural Substitution for Branch-level Network Re-parameterization
Seungmin Oh, Jongbin Ryu
Asian Conference on Computer Vision (ACCV), 2024.
Unsupervised feature learning for self-tuning neural networks.
Jongbin Ryu, Ming-Hsuan Yang, and Jongwoo Lim.
Neural Networks, 2021.
Visual applications
Unsupervised Hashing Network with Hyper Quantization Tree
SungEun Kim, Jongbin ryu
British Machine Vision Conference (BMVC), 2024.
Unsupervised Face Domain Transfer for Low-Resolution Face Recognition
Sungeun Hong and Jongbin Ryu* (corresponding author)
IEEE Signal Processing Letters (SPL), 2020
SweepNet: Wide-baseline Omnidirectional Depth Estimation
Changhee Won, Jongbin Ryu, and Jongwoo Lim
IEEE International Conference on Robotics and Automation (ICRA), 2019
OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
Changhee Won, Jongbin Ryu, and Jongwoo Lim
IEEE International Conference on Computer Vision (ICCV), 2019
Joint Object Tracking and Segmentation with Independent Convolutional Neural Networks
Hakjin Lee, Jongbin Ryu, and Jongwoo Lim
Coview: 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, in conjuction with ACM MM, 2018
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