Publications
2024
Hyeonwoo Cho, Chanmin Park, Dong-hee Kim, Jinyoung Kim, Won Hwa Kim, “CNG-SFDA: Clean-and-Noisy Region Guided Online-Offline Source-Free Domain Adaptation”, Asian Conference on Computer Vision (ACCV), 2024
Wooseok Jung, Saehyun Kim, Dong-Hee Kim, Won Hwa Kim, “Modelling Brain Atrophy Dynamics Enhances Predicting Cognitive Decline in Alzheimer’s Disease Continuum”, Cognitive Computational Neuroscience (CCN), 2024.
Seunghun Baek*, Jaeyoon Sim*, Guorong Wu, Won Hwa Kim, “OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024. [*: equal contribution, Provisional accept: ~11%]
Minjae Jeong*, Hyuna Cho*, Sungyoon Jung, Won Hwa Kim, “Uncertainty-aware Diffusion-based Adversarial Attack for Realistic Colonoscopy Image Synthesis”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024. [*: equal contribution, Provisional accept: ~11%]
Yanquan Huang, Tingting Dan, Won Hwa Kim, Guorong Wu, “Uncovering Cortical Pathways of Prion-like Pathology Spreading in Alzheimer’s Disease by Neural Optimal Mass Transport”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024. [Provisional accept: ~11%]
Yechan Hwang, Soojin Hwang, Guorong Wu, Won Hwa Kim, “Multi-order Simplex-based Graph Neural Network for Brain Network Analysis”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.
Jaeyoon Sim, Minjae Lee, Guorong Wu, Won Hwa Kim, “Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.
Sooyeon Jeon, Yujee Song, Won Hwa Kim, “Gene-to-Image: Decoding Brain Images from Genetics via Latent Diffusion Models”, MICCAI Workshop on PRedictive Intelligence in MEdicine (PRIME), 2024.
Tingting Dan, Mustafa Dere, Won Hwa Kim, Minjeong Kim, Guorong Wu, “TauFlowNet: Revealing latent propagation mechanism of tau aggregates using deep neural transport equations”, Medical Image Analysis (MedIA), 2024.
Hyuna Cho, Jaeyoon Sim, Guorong Wu, Won Hwa Kim, “Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization”, International Conference on Machine Learning (ICML), 2024.
Tingting Dan, Ziquan Wei, Won Hwa Kim, Guorong Wu, “Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold”, International Conference on Machine Learning (ICML), 2024.
Seunghun Baek*, Jaeyoon Sim*, Mustafa Dere, Minjeong Kim, Guorong Wu, Won Hwa Kim, “Modality-Agnostic Style Transfer for Holistic Feature Imputation”, International Symposium on Biomedical Imaging (ISBI), 2024. [Oral presentation: ~14%, *: equal contribution]
Yujee Song, Donghyun Lee, Rui Meng, Won Hwa Kim, “Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations”, International Conference on Learning Representations (ICLR), 2024.
Inhyuk Park, Won Hwa Kim, Jongbin Ryu, “Style-KD: Class-imbalanced medical image classification via style knowledge distillation”, Biomedical Signal Processing and Control, 2024. [Impact factor: 5.1]
Jaeyoon Sim, Sooyeon Jeon, Injun Choi, Guorong Wu, Won Hwa Kim, “Learning to Approximate Adaptive Kernel Convolution on Graphs”, AAAI Conference on Artificial Intelligence (AAAI), 2024.
Hyuna Cho, Yubin Han, Amal Isaiah, Won Hwa Kim, “Covariate Correcting Network for Isolating the Impact of Long-term SES Changes on Brain Development”, Annual Meeting of the Organization for Human Brain Mapping (OHBM)
Joonhyuk Park*, Yechan Hwang*, Minjeong Kim, Moo K. Chung, Guorong Wu, Won Hwa Kim, “Brain Connectome Analysis for Alzheimer’s Disease using Hodge Laplacian-based Edge Convolution”, Annual Meeting of the Organization for Human Brain Mapping (OHBM) [*: equal contribution]
Hyuna Cho, Injun Choi, Suha Kwak, Won Hwa Kim, “Interactive Network Perturbation between Teacher and Students for Semi-Supervised Semantic Segmentation”, Winter Conference on Applications of Computer Vision (WACV), 2024. [Accepted in the first round: 92/815 = ~11%]
2023
Hyuna Cho, Minjae Jeong, Sooyeon Jeon, Sungsoo Ahn, Won Hwa Kim, “Multi-resolution Spectral Coherence for Graph Generation with Score-based Diffusion”, Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.
Tingting Dan, Jiaqi Ding, Ziquan Wei, Shahar Z Kovalsky, Minjeong Kim, Won Hwa Kim, Guorong Wu, “Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals”, Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.
Tingting Dan, Minjeong Kim, Won Hwa Kim, Guorong Wu, “Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics”, IEEE Transactions on Medical Imaging (TMI), 2023.
Hyuna Cho, Guorong Wu, Won Hwa Kim, “Mixing Temporal Graphs with MLP for Longitudinal Brain Connectome Analysis”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023. [Oral Presentation: 68/2250 = ~3%]
Hyuna Cho, Yubin Han, Won Hwa Kim, “Anti-Adversarial Consistency Regularization for Data Augmentation: Applications to Robust Medical Image Segmentation”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023. [Provisional Accept: ~14%]
Joonhyuk Park*, Yechan Hwang*, Minjeong Kim, Moo K. Chung, Guorong Wu, Won Hwa Kim, “Convolving Directed Graph Edges via Hodge Laplacian for Brain Network Analysis”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023. [Provisional Accept: ~14%, *: equal contribution]
Ellen Jieun Oh, Yechan Hwang, Yubin Han, Taegeun Choi, Geunyoung Lee, Won Hwa Kim, “RESToring Clarity: Unpaired Retina Image Enhancement using Scattering Transform”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.
Tingting Dan, Minjeong Kim, Won Hwa Kim, Guorong Wu, “Enhance Early Diagnosis Accuracy of Alzheimer’s Disease by Elucidating Interactions between Amyloid Cascade and Tau Propagation”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.
Tingting Dan, Minjeong Kim, Won Hwa Kim, Guorong Wu, “TauFlowNet: Uncovering Propagation Mechanism of Tau Aggregates by Neural Transport Equation”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.
Tingting Dan, Minjeong Kim, Won Hwa Kim, Guorong Wu, “Uncovering Structural-Functional Coupling Alterations for Neurodegenerative Diseases”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023.
Jinhyeok Jang, Woo-han Yun, Won Hwa Kim, Youngwoo Yoon, Jaehong Kim, Jaeyeon Lee, ByungOk Han, “Learning to Boost Training by Periodic Nowcasting Near Future Weights”, International Conference on Machine Learning (ICML), 2023.
Rui Meng, Fan Yang, Won Hwa Kim, “Dynamic Covariance Estimation via Predictive Wishart Process with an Application on Brain Connectivity Estimation”, Computational Statistics & Data Analysis (CSDA), 2023.
Hyuna Cho, Guorong Wu, Won Hwa Kim, “Spatio-Temporal Multi-Layer Perceptron for Longitudinal Brain Connectome Analysis”, Annual Meeting of the Organization for Human Brain Mapping (OHBM) , 2023.
Deunsol Jung, Sanghyun Kim, Won Hwa Kim, Minsu Cho, “Devil’s on the Edges: Selective Quad Attention for Scene Graph Generation”, Computer Vision and Pattern Recognition (CVPR), 2023.
Huan Liu, Tingting Dan, Zhuobin Huang, Defu Yang, Won Hwa Kim, Minjeong Kim, Paul Laurienti, Guorong Wu, “HoloBrain: A Harmonic Holography for Self-organized Brain Function”, Information Processing in Medical Imaging (IPMI), 2023. [*: equal contribution]
Seunghun Baek, Injun Choi, Mustafa Dere, Minjeong Kim, Guorong Wu, Won Hwa Kim, “Learning Covariance-based Multi-scale Representation of NeuroImaging Measures for Alzheimer Classification”, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.
2022
Injun Choi, Guorong Wu, Won Hwa Kim, “How Much to Aggregate: Learning Adaptive Node-wise Scales on Graphs for Brain Networks”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022. [Acceptance rate: 31%]
Tingting Dan, Hongmin Cai, Zhuobin Huang, Paul Laurenti, Won Hwa Kim, Guorong Wu, “Neuro-RDM: An Explainable Neural Network Landscape of Reaction-Diffusion Model for Cognitive Task Recognition”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022. [Acceptance rate: 31%]
Gangin Park, Chunsan Hong, Bohyung Kim, Won Hwa Kim, “What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?”, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE), 2022.
Xin Ma, Won Hwa Kim, “Locally Normalized Soft Contrastive Clustering for Compact Clusters”, International Joint Conference on Artificial Intelligence (IJCAI), 2022. [Acceptance rate: 15%]
Hyuna Cho*, Feng Tong, Sungyong You, Sungyoung Jung, Won Hwa Kim, Jayoung Kim, “Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology”, IEEE Open Journal of Engineering in Medicine and Biology (OJEMB), 2022 [*: Hyuna advised by WHKim]
Hyuna Cho, Gunwoong Park, Amal Isaiah, Won Hwa Kim, “Covariate Correcting Network for Detecting Sole Effect of Socioeconomic Status on Brain in Children”, Annual Meeting of the Organization for Human Brain Mapping (OHBM), 2022.
Fan Yang, Guorong Wu, Won Hwa Kim, “Disentangled Representation of Longitudinal β-Amyloid for AD via Sequential Graph Variational Autoencoder with Supervision”, IEEE International Symposium on Biomedical Imaging (ISBI), 2022.
2021
Hyuna Cho, Gunwoong Park, Amal Isaiah, Won Hwa Kim, “Covariate Correcting Networks for Identifying Associations between Socioeconomic Factors and Brain Outcomes in Children”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.
Fan Yang*, Rui Meng*, Hyuna Cho, Guorong Wu, Won Hwa Kim, “Disentangled Sequential Graph Autoencoder for Preclinical Alzheimer’s Disease Characterizations from ADNI study”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021. [*: Equal contribution]
Xin Ma, Guorong Wu, Seongjae Hwang, Won Hwa Kim, “Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders”, International Conference on Information Processing in Medical Imaging (IPMI), 2021.
Debapriya Banerjee, Maria Kyrarini, Won Hwa Kim, “Image-Label Recovery on Fashion Data Using Image Similarity from Triple Siamese Network ”, Technologies, 2021.
2020
ByungOk Han, Woo-han Yun, Jang-hee Yoo, Won Hwa Kim, “Toward Unbiased Facial Expression Recognition in the Wild via Cross-dataset Adaptation”, IEEE Access, 2020.
Gowtham Krishnan Murugesan, Chandan Ganesh, Sahil Nalawade, Elizabeth M. Davenport, Ben Wagner, Won Hwa Kim, Joseph A. Maldjian, “BrainNET: Inference of Brain Network Topology using Machine Learning”, Brain Connectivity, 2020.
Tuan Q. Dinh, Yunyang Xiongy, Zhichun Huangy, Tien Voy, Akshay Mishray, Won Hwa Kim, Sathya N. Ravi, Vikas Singh, “Performing Group Difference Testing on Graph Structured Data from GANs: Analysis and Applications in Neuroimaging”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
Fan Yang, Amal Isaiah, Won Hwa Kim, “COVLET: Covariance-based Wavelet-like Transform for Statistical Analysis of Brain Characteristics in Children”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020.
Feng Tong*, Muhammad Shahid, Peng Jin, Sungyong Jung, Won Hwa Kim, Jayoung Kim “Classification of the Urinary Metabolome using Machine Learning and Potential Applications to Diagnosing Interstitial Cystitis”, Bladder, 2020. [*: Feng advised by WHKim]
Jayoung Kim, Peng Jin, Won Hwa Kim, Wun-Jae Kim “Utilizing Machine Learning to Discern Hidden Clinical Values from Big Data in Urology”, Investigative and Clinical Urology, 2020.
Xin Ma, Guorong Wu, Won Hwa Kim, “Enriching Statistical Inferences on Brain Connectivity via Latent Space Graph Embeddings”, Annual Meeting of the Organization for Human Brain Mapping (OHBM) , 2020.
Xin Ma, Guorong Wu, Won Hwa Kim, “Multi-resolution Graph Neural Network for Detecting Variations in Brain Connectivity”, Annual Meeting of the Organization for Human Brain Mapping (OHBM) , 2020.
Xin Ma, Guorong Wu, Won Hwa Kim, “Enriching Statistical Inferences on Brain Connectivity for Alzheimer’s Disease Analysis via Latent Space Graph Embedding”, IEEE International Symposium on Biomedical Imaging (ISBI), 2020. [Accepted for Oral Presentation]
Xin Ma, Guorong Wu, Won Hwa Kim, “Multi-resolution Graph Neural Network for Detecting Variations in Brain Connectivity”, Interaction of Geometry and Topology in Biomedical Imaging (ISBI Workshop), 2020.
Anna Philips, Farah Naz, Kate Kyung Hyun, Vivek Patel, Gorden G. Zhang, Won Hwa Kim, “Social Media Text Analysis using Multi-kernel Convolution Neural Network for Ride Hailing Service Assessment”, Transportation Research Board (TRB) Annual Meeting, 2020.
2019
Seongjae Hwang, Zirui Tao, Won Hwa Kim*, Vikas Singh*, “Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging” International Conference on Computer Vision (ICCV), 2019. [*: Senior authorship shared]
Seongjae Hwang, Zirui Tao, Won Hwa Kim*, Vikas Singh*, “Statistical Analysis of Longitudinally and Conditionally Generated Neuroimaging Measures via Conditional Recurrent Flow”, Statistical Deep Learning in Computer Vision (ICCV Workshop), 2019. [*: Senior authorship shared]
Won Hwa Kim, Annie M. Racine, Nagesh Adluru, Seong Jae Hwang, Kaj Blennow, Henrik Zetterberg, Cynthia M.Carlsson, Sanjay Asthana, Rebecca L. Koscik, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh, “Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis” NeuroImage: Clinical, 2019.
Annie M. Racine, Andrew P. Merluzzi, Nagesh Adluru, Derek Norton, Rebecca L. Koscik, Lindsay R. Clark, Sara E. Berman, Christopher R. Nicholas, Sanjay Asthana, Andrew L. Alexander, Kaj Blennow, Henrik Zetterberg, Won Hwa Kim, Vikas Singh, Cynthia M. Carlsson, Barbara B. Bendlin, Sterling C. Johnson, “Association of longitudinal whitematter degeneration and cerebrospinal fluid biomarkers of neurodegeneration, inflammation and Alzheimer’s disease in late-middle-aged adults”, Brain Imaging and Behavior, 2019.
Seong Jae Hwang, Nagesh Adluru, Won Hwa Kim, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh, “Associations between PET Amyloid Pathology and DTI Brain Connectivity in Preclinical Alzheimer’s Disease”, Brain Connectivity, 2019.
Won Hwa Kim, Noelle Fields, Ling Xu, and Chen Kan, “Missing Value Imputation via Graph Completion in Questionnaires of Persons with Dementia”, Gerontological Society of America (GSA) Annual Scientific Meeting, 2019.
Zachary Bailey, Xin Ma, Martin Hirsch, Won Hwa Kim, Juhyun Lee, “Development of an Auto-segmentation Technique using a Convolution Neural Network for the Segmentation of the Vantricular Cavity in Zebrafish”, Basic Cardiovascular Sciences, 2019.
2018
Won Hwa Kim, Hyunwoo J. Kim, Nagesh Adluru, Vikas Singh, “Multi-resolution Analysis for Sparse Inverse Covariance Matrix Estimation”, International Conference on Brain Informatics (BI), 2018.
2017
Won Hwa Kim, “A Multi-resolution Framework for Statistical Analysis of Neuroimaging Data”, Doctoral Thesis, 2017.
Won Hwa Kim, Seongjae Hwang, Nagesh Adluru, Sterling C. Johnson, Vikas Singh, ” Graph Completion: A Generalization of Netflix Prize Problem to Designing Cost Effective Neuroimaging Trials in Preclinical AD”, Alzheimer’s Association International Conference (AAIC), 2017.
Won Hwa Kim, Mona Jalal, Seongjae Hwang, Sterling C. Johnson, Vikas Singh, “Online Graph Completion: Multivariate Signal Recovery in Computer Vision”, Computer Vision and Pattern Recognition (CVPR), 2017.
2016
Won Hwa Kim, Seong Jae Hwang, Nagesh Adluru, Sterling C. Johnson, Vikas Singh, “Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging”, European Conference on Computer Vision (ECCV), 2016.
Seong Jae Hwang, Won Hwa Kim, Barbara B. Bendlin, Nagesh Adluru, Vikas Singh, “Multi-Resolution Analysis of DTI-Derived Brain Connectivity and the Influence of PET-Derived Alzheimer’s Disease Pathology in a Preclinical Cohort”, Alzheimer’s Association International Conference (AAIC), 2016.
Won Hwa Kim*, Hyunwoo J. Kim*, Nagesh Adluru, Vikas Singh, “Latent Variable Graphical Model Selection using Harmonic Analysis: Applications to the Human Connectome Project (HCP)”, Computer Vision and Pattern Recognition (CVPR), 2016. [*: First authorship share, Spotlight: ~9.7%] *: First authorship shared.
2015
Won Hwa Kim, Sathya Ravi, Sterling C. Johnson, Ozioma Okonkwo, Vikas Singh, “On Statistical Analysis of NeuroImages with Imperfect Registration”, International Conference on Computer Vision (ICCV), 2015.
Won Hwa Kim, Nagesh Adluru, Moo K. Chung, Ozioma C. Okonkwo, Sterling C. Johnson, Barbara B. Bedlin, Vikas Singh, “Multi-resolution Statistical Analysis of Brain Connectivity Graphs in Preclinical Alzheimer’s Disease”, NeuroImage, 2015.
Won Hwa Kim, Nagesh Adluru, Moo K. Chung, Ozioma C. Okonkwo, Sterling C. Johnson, Barbara B. Bedlin, Vikas Singh, “Multi-resolution Statistical Analysis of Brain Connectivity Graphs in Preclinical Alzheimer’s Disease”, Alzheimer’s Association International Conference (AAIC), 2015.
Won Hwa Kim, Barbara B. Bendlin, Moo K. Chung, Sterling C. Johnson, Vikas Singh, “Statistical Inference Models for Image Datasets with Systematic Variations”, Computer Vision and Pattern Recognition (CVPR), 2015.
Won Hwa Kim, Vikas Singh, Moo K. Chung, Nagesh Adluru, Barbara B. Bendlin, Sterling C. Johnson, “Multi-resolution statistical analysis on graph structured data in Neuroimaging”, International Symposium on Biomedical Imaging (ISBI), 2015 (Invited paper / Oral presentation)
2014
Won Hwa Kim, Vikas Singh, Moo K. Chung, Chris Hinrichs, Deepti Pachauri, Ozioma C. Okonkwo, Sterling C. Johnson, “Multi-resolutional Shape Features via non-Euclidean Wavelets: Applications to Statistical Analysis of Cortical Thickness”, NeuroImage, 2014.
A. Pasha Hosseinbor, Won Hwa Kim, Nagesh Adluru, Amit Acharya, Houri K. Vorperian, Moo. K. Chung, “The 4D Hyperspherical Diffusion Wavelet: a New Method for the Detection of Localized Anatomical Variation”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2014.
2013
Won Hwa Kim, Nagesh Adluru, Moo K. Chung, Sylvia Charchut, Johnson J. GadElkarim, Lori Altshuler, Teena Moody, Anand Kumar, Vikas Singh, and Alex D. Leow, “Multi-resolutional Brain Network Filtering and Analysis via Wavelets on Non-Euclidean Space”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2013.
Won Hwa Kim, Moo K. Chung, Vikas Singh, “Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems”, Computer Vision and Pattern Recognition (CVPR), 2013.
2012
Won Hwa Kim, Deepti Pachauri, Charles Hatt, Moo K. Chung, Sterling C. Johnson, Vikas Singh, “Wavelet Based Multi-scale Shape Features on Arbitrary Surfaces for Cortical Thickness Discrimination”, Advances in Neural Information Processing Systems (NIPS), 2012.
2010 and before
Won Hwa Kim, Jeong Woo Park, Woo Hyun Kim, Wong Hyong Lee, Myung Jin Chung, “Proposal of 2D Mood Model for Human-like Behaviors of Robot”, The Journal of Korea Robotics Society, 2010.
Won Hwa Kim, Jeong Woo Park, Won Hyong Lee, Woo Hyun Kim, Myung Jin Chung, ” Stochastic Approach on a Simplified OCC Model for Uncertainty and Believability”, International Conference on Computational Intelligence in Robotics and Automation (CIRA), 2009.
Jeongwoo Park, Won Hwa Kim, Wong Hyong Lee, Myung Jin Chung, “A Robot Simulator’FRESi’for Dynamic Facial Expression”, International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2009.
Jeongwoo Park, Woo Hyun Kim, Wong Hyong Lee, Won Hwa Kim, Myung Jin Chung, “Lifelike Facial Expression of Mascot-type Robot based on Emotional Boundaries”, Robotics and Biomimetics (ROBIO), 2009.
Woo Hyun Kim, Jeongwoo Park, Wong Hyong Lee, Won Hwa Kim, Myung Jin Chung, “Synchronized Multimodal Expression Generation using Editing Toolkit for a Human-friendly robot”, Robotics and Biomimetics (ROBIO), 2009.
Publications (Domestic)
Yujee Song, Donghyun Lee, Rui Meng, Won Hwa Kim, “Explainable and Continuous Time Modeling of Marked Temporal Point Process”, Workshop on Image Processing and Image Understanding (IPIU), 2024.
Jaeyoon Sim, Sooyeon Jeon, Yubin Han, Won Hwa Kim, “Learning to Approximate Adaptive Kernel Convolution on Graphs”, Workshop on Image Processing and Image Understanding* (IPIU), 2024. [Bronze Award]
Seunghun Baek, Jaeyoon Sim, Siyeon Park, Cheolung Yang, Songyi Jeon, Jongho Song, Won Hwa Kim, “AI-Driven BGA Solder Joint Failure Detection of PCB Assembly”, Autumn Annual Conference of Institute of Electronics and Information Engineers (IEIE), 2023
Hyuna Cho, Yubin Han, Guorong Wu, Won Hwa Kim, “Spatio-Temporal Multi-Layer Perceptron for Longitudinal Human Brain Connectome Analysis”, Workshop on Image Processing and Image Understanding (IPIU), 2023. [Oral Presentation / Silver Award]
Hyuna Cho, Injun Choi, Yechan Hwang, Suha Kwak, Won Hwa Kim, “Interactive Network Perturbation for Semi-Supervised Semantic Segmentation”, Workshop on Image Processing and Image Understanding (IPIU), 2023. [Oral Presentation / Bronze Award]
Ellen Jieun Oh, Taegeun Choi, Geunyoung Lee, Won Hwa Kim, “Unpaired Retina Image Enhancement using Wavelet Scattering Transfom”, Workshop on Image Processing and Image Understanding (IPIU), 2023. [Encouragement Award]
Seunghun Baek, Injun Choi, Won Hwa Kim, “Learning Covariance-based Multi-scale Representation of NeuroImaging Measures for Alzheimer Classification”, Workshop on Image Processing and Image Understanding (IPIU), 2023.