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ORCIDSeong Tae Kim 0001
Seong-Tae Kim 0001 – 김성태
Person information
- unicode name: 김성태
- affiliation: Kyung Hee University, South Korea
- affiliation (former): Technical University of Munich, Department of Informatics, Germany
- affiliation (former): KAIST, Image and Video Systems Laboratory, Daejeon, South Korea
Other persons with the same name
- Seong-Tae Kim (aka: Seong Tae Kim) — disambiguation page
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Journal Articles
- 2024
[j15]Chu Myaet Thwal
, Minh N. H. Nguyen, Ye Lin Tun, Seong Tae Kim, My T. Thai, Choong Seon Hong:
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning. Neural Networks 170: 635-649 (2024)- 2023
- [j14]Felix Buchert, Nassir Navab, Seong Tae Kim:
Toward Label-Efficient Neural Network Training: Diversity-Based Sampling in Semi-Supervised Active Learning. IEEE Access 11: 5193-5205 (2023) - [j13]Samra Irshad, Douglas P. S. Gomes, Seong Tae Kim:
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks. IEEE Access 11: 35097-35110 (2023) - [j12]Ah-Hyung Shin, Seong Tae Kim, Gyeong-Moon Park:
Time Series Anomaly Detection Using Transformer-Based GAN With Two-Step Masking. IEEE Access 11: 74035-74047 (2023) - [j11]Michelle Xiao-Lin Foo, Seong Tae Kim, Magdalini Paschali, Leili Goli, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler:
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT Scans. IEEE Access 11: 77596-77607 (2023) - [j10]Enki Cho, Minkuk Kim, Hyung-Il Kim, Jinyoung Moon, Seong Tae Kim:
Exploiting recollection effects for memory-based video object segmentation. Image Vis. Comput. 140: 104866 (2023) - [j9]Muhammad Asif Razzaq, Jamil Hussain, Jaehun Bang, Cam-Hao Hua, Fahad Ahmed Satti, Ubaid Ur Rehman, Hafiz Syed Muhammad Bilal, Seong Tae Kim, Sungyoung Lee:
A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions. Sensors 23(9): 4373 (2023) - 2022
- [j8]Junho Kim, Seongyeop Kim, Seong Tae Kim, Yong Man Ro:
Robust Perturbation for Visual Explanation: Cross-Checking Mask Optimization to Avoid Class Distortion. IEEE Trans. Image Process. 31: 301-313 (2022) - 2021
- [j7]Seong Tae Kim, Umut Küçükaslan, Nassir Navab:
Longitudinal Brain MR Image Modeling Using Personalized Memory for Alzheimer's Disease. IEEE Access 9: 143212-143221 (2021) - [j6]Jung Uk Kim, Seong-Tae Kim, Hong Joo Lee, Sangmin Lee, Yong Man Ro:
CUA Loss: Class Uncertainty-Aware Gradient Modulation for Robust Object Detection. IEEE Trans. Circuits Syst. Video Technol. 31(9): 3529-3543 (2021) - 2020
- [j5]Leslie Ching Ow Tiong, Seong Tae Kim, Yong Man Ro:
Multimodal facial biometrics recognition: Dual-stream convolutional neural networks with multi-feature fusion layers. Image Vis. Comput. 102: 103977 (2020) - [j4]Maria Tirindelli, Maria Victorova, Javier Esteban, Seong Tae Kim, David Navarro-Alarcon, Yong-Ping Zheng, Nassir Navab:
Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level". IEEE Robotics Autom. Lett. 5(4): 5661-5668 (2020) - [j3]Hong Joo Lee, Seong Tae Kim, Hakmin Lee, Yong Man Ro:
Lightweight and Effective Facial Landmark Detection using Adversarial Learning with Face Geometric Map Generative Network. IEEE Trans. Circuits Syst. Video Technol. 30(3): 771-780 (2020) - 2019
- [j2]Leslie Ching Ow Tiong, Seong Tae Kim, Yong Man Ro:
Implementation of multimodal biometric recognition via multi-feature deep learning networks and feature fusion. Multim. Tools Appl. 78(16): 22743-22772 (2019) - [j1]Seong Tae Kim, Yong Man Ro:
Attended Relation Feature Representation of Facial Dynamics for Facial Authentication. IEEE Trans. Inf. Forensics Secur. 14(7): 1768-1778 (2019)
Conference and Workshop Papers
- 2024
- [c38]Minkuk Kim, Hyeon Bae Kim, Jinyoung Moon, Jinwoo Choi, Seong Tae Kim:
Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval. CVPR 2024: 13894-13904 - [c37]Youngmin Oh, Hyung-Il Kim, Seong Tae Kim, Jung Uk Kim:
MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection. ECCV (10) 2024: 326-345 - 2023
- [c36]Yong Hyun Ahn, Gyeong-Moon Park, Seong Tae Kim:
LINe: Out-of-Distribution Detection by Leveraging Important Neurons. CVPR 2023: 19852-19862 - [c35]Jung Uk Kim, Seong Tae Kim:
Towards Robust Audio-Based Vehicle Detection Via Importance-Aware Audio-Visual Learning. ICASSP 2023: 1-5 - 2022
- [c34]Felix Buchert, Nassir Navab, Seong Tae Kim:
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning. ICPR 2022: 2063-2069 - 2021
- [c33]Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab:
Neural Response Interpretation Through the Lens of Critical Pathways. CVPR 2021: 13528-13538 - [c32]Tetiana Klymenko, Seong Tae Kim, Kirsten Lauber, Christopher Kurz, Guillaume Landry, Nassir Navab, Shadi Albarqouni:
Butterfly-Net: Spatial-Temporal Architecture For Medical Image Segmentation. ISBI 2021: 616-620 - [c31]Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler:
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs. MICCAI (7) 2021: 273-282 - [c30]Ashkan Khakzar, Yang Zhang, Wejdene Mansour, Yuezhi Cai, Yawei Li, Yucheng Zhang, Seong Tae Kim, Nassir Navab:
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features. MICCAI (3) 2021: 391-401 - [c29]Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger, Icxel Valeriano Quiroz, Nikolaus Pinger, Soroosh Baselizadeh, Seong Tae Kim, Nassir Navab:
Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models. MICCAI (3) 2021: 499-508 - [c28]Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong Tae Kim, Benjamin Busam, Nassir Navab:
OperA: Attention-Regularized Transformers for Surgical Phase Recognition. MICCAI (4) 2021: 604-614 - [c27]Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim, Nassir Navab:
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information. NeurIPS 2021: 20040-20051 - 2020
- [c26]Hakmin Lee, Hong Joo Lee, Seong Tae Kim, Yong Man Ro:
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack. BMVC 2020 - [c25]Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong-Tae Kim, Nassir Navab:
Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation. BrainLes@MICCAI (1) 2020: 111-121 - [c24]Jung Uk Kim, Seong Tae Kim, Eun Sung Kim, Sang-Keun Moon, Yong Man Ro:
Towards High-Performance Object Detection: Task-Specific Design Considering Classification and Localization Separation. ICASSP 2020: 4317-4321 - [c23]Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab:
TeCNO: Surgical Phase Recognition with Multi-stage Temporal Convolutional Networks. MICCAI (3) 2020: 343-352 - 2019
- [c22]Hyebin Lee, Seong Tae Kim, Yong Man Ro:
Building a Breast-Sentence Dataset: Its Usefulness for Computer-Aided Diagnosis. ICCV Workshops 2019: 440-449 - [c21]Jae-Hyeok Lee, Seong Tae Kim, Yong Man Ro:
Probenet: Probing Deep Networks. ICIP 2019: 3821-3825 - [c20]Seong Tae Kim, Jae-Hyeok Lee, Yong Man Ro:
Visual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis. Computer-Aided Diagnosis 2019: 109500K - [c19]Hyebin Lee, Seong Tae Kim, Yong Man Ro:
Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis. iMIMIC/ML-CDS@MICCAI 2019: 21-29 - [c18]Hakmin Lee, Seong Tae Kim, Jae-Hyeok Lee, Yong Man Ro:
Realistic Breast Mass Generation Through BIRADS Category. MICCAI (6) 2019: 703-711 - 2018
- [c17]Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee, Yong Man Ro:
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation. ECCV Workshops (6) 2018: 326-334 - [c16]Seong Tae Kim, Yong Man Ro:
Facial Dynamics Interpreter Network: What Are the Important Relations Between Local Dynamics for Facial Trait Estimation? ECCV (12) 2018: 475-491 - [c15]Seong Tae Kim, Hakmin Lee, Hak Gu Kim, Yong Man Ro:
ICADx: interpretable computer aided diagnosis of breast masses. Computer-Aided Diagnosis 2018: 1057522 - [c14]Tae Kwan Lee, Wissam J. Baddar, Seong Tae Kim, Yong Man Ro:
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN. MMM (1) 2018: 117-129 - [c13]Hong Joo Lee, Wissam J. Baddar, Hak Gu Kim, Seong Tae Kim, Yong Man Ro:
Teacher and Student Joint Learning for Compact Facial Landmark Detection Network. MMM (1) 2018: 493-504 - 2017
- [c12]Seong Tae Kim, Yeoreum Choi, Yong Man Ro:
Multi-Scale Facial Scanning via Spatial Lstm for Latent Facial Feature Representation. BIOSIG 2017: 127-135 - [c11]Geonmo Gu, Seong Tae Kim, Yong Man Ro:
Adaptive attention fusion network for visual question answering. ICME 2017: 997-1002 - 2016
- [c10]Seong-Tae Kim, Dae Hoe Kim, Yong Man Ro:
Facial dynamic modelling using long short-term memory network: Analysis and application to face authentication. BTAS 2016: 1-6 - [c9]Dae Hoe Kim, Seong-Tae Kim, Yong Man Ro:
Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis. ICASSP 2016: 927-931 - [c8]Seong-Tae Kim, Dae Hoe Kim, Yong Man Ro:
Spatio-temporal representation for face authentication by using multi-task learning with human attributes. ICIP 2016: 2996-3000 - [c7]Wissam J. Baddar, Jisoo Son, Dae Hoe Kim, Seong-Tae Kim, Yong Man Ro:
A deep facial landmarks detection with facial contour and facial components constraint. ICIP 2016: 3209-3213 - 2015
- [c6]Seong-Tae Kim, Dae Hoe Kim, Dong Jin Ji, Yong Man Ro:
Region matching based on local structure information in ipsilateral digital breast tomosynthesis views. ICIP 2015: 252-256 - [c5]Dae Hoe Kim, Seong-Tae Kim, Wissam J. Baddar, Yong Man Ro:
Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume. ICIP 2015: 4521-4524 - [c4]Seong Tae Kim, Dae Hoe Kim, Yong Man Ro:
Combination of conspicuity improved synthetic mammograms and digital breast tomosynthesis: a promising approach for mass detection. Computer-Aided Diagnosis 2015: 941419 - [c3]Dae Hoe Kim, Seong Tae Kim, Yong Man Ro:
Feature extraction from inter-view similarity of DBT projection views. Computer-Aided Diagnosis 2015: 941425 - 2014
- [c2]Seong-Tae Kim, Dae Hoe Kim, Eun Suk Cha, Yong Man Ro:
Mass detection based on pooled mass probability map of 3D reconstructed slices in digital breast tomosynthesis. BHI 2014: 57-60 - [c1]Seong-Tae Kim, Dae Hoe Kim, Yong Man Ro:
Generation of conspicuity-improved synthetic image from digital breast tomosynthesis. DSP 2014: 395-399
Informal and Other Publications
- 2024
- [i34]Chu Myaet Thwal, Minh N. H. Nguyen, Ye Lin Tun, Seong Tae Kim, My T. Thai, Choong Seon Hong:
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning. CoRR abs/2401.11652 (2024) - [i33]Yong Hyun Ahn, Hyeon Bae Kim, Seong Tae Kim:
WWW: A Unified Framework for Explaining What, Where and Why of Neural Networks by Interpretation of Neuron Concepts. CoRR abs/2402.18956 (2024) - [i32]Minkuk Kim, Hyeon Bae Kim, Jinyoung Moon, Jinwoo Choi, Seong Tae Kim:
Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval. CoRR abs/2404.07610 (2024) - [i31]Hyeon Bae Kim, Yong Hyun Ahn, Seong Tae Kim:
Mask-Free Neuron Concept Annotation for Interpreting Neural Networks in Medical Domain. CoRR abs/2407.11375 (2024) - [i30]Youngmin Oh, Hyung-Il Kim, Seong Tae Kim, Jung Uk Kim:
MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection. CoRR abs/2407.16448 (2024) - 2023
- [i29]Yong Hyun Ahn, Gyeong-Moon Park, Seong Tae Kim:
LINe: Out-of-Distribution Detection by Leveraging Important Neurons. CoRR abs/2303.13995 (2023) - [i28]Chaoning Zhang, Chenshuang Zhang, Chenghao Li, Yu Qiao, Sheng Zheng, Sumit Kumar Dam, Mengchun Zhang, Jung Uk Kim, Seong Tae Kim, Jinwoo Choi, Gyeong-Moon Park, Sung-Ho Bae, Lik-Hang Lee, Pan Hui, In So Kweon, Choong Seon Hong:
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era. CoRR abs/2304.06488 (2023) - 2022
- [i27]Tobias Czempiel, Coco Rogers, Matthias Keicher, Magdalini Paschali, Rickmer Braren, Egon Burian, Marcus R. Makowski, Nassir Navab, Thomas Wendler, Seong Tae Kim:
Longitudinal Self-Supervision for COVID-19 Pathology Quantification. CoRR abs/2203.10804 (2022) - [i26]Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab:
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models. CoRR abs/2204.01729 (2022) - [i25]Felix Buchert, Nassir Navab, Seong Tae Kim:
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning. CoRR abs/2207.12302 (2022) - [i24]Samra Irshad, Douglas P. S. Gomes, Seong Tae Kim:
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks. CoRR abs/2210.04285 (2022) - 2021
- [i23]Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong-Tae Kim, Benjamin Busam, Nassir Navab:
OperA: Attention-Regularized Transformers for Surgical Phase Recognition. CoRR abs/2103.03873 (2021) - [i22]Seong-Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler:
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs. CoRR abs/2103.07240 (2021) - [i21]Aadhithya Sankar, Matthias Keicher, Rami Eisawy, Abhijeet Parida, Franz Pfister, Seong-Tae Kim, Nassir Navab:
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images. CoRR abs/2103.10868 (2021) - [i20]Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab:
Neural Response Interpretation through the Lens of Critical Pathways. CoRR abs/2103.16886 (2021) - [i19]Ashkan Khakzar, Yang Zhang, Wejdene Mansour, Yuezhi Cai, Yawei Li, Yucheng Zhang, Seong Tae Kim, Nassir Navab:
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features. CoRR abs/2104.00411 (2021) - [i18]Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger, Icxel Valeriano Quiroz, Nikolaus Pinger, Soroosh Baselizadeh, Seong Tae Kim, Nassir Navab:
Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models. CoRR abs/2104.02481 (2021) - [i17]Michelle Xiao-Lin Foo, Seong Tae Kim, Magdalini Paschali, Leili Goli, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler:
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans. CoRR abs/2110.00948 (2021) - [i16]Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim, Nassir Navab:
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information. CoRR abs/2110.01471 (2021) - 2020
- [i15]Maria Tirindelli, Maria Victorova, Javier Esteban, Seong Tae Kim, David Navarro-Alarcon, Yong-Ping Zheng, Nassir Navab:
Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level". CoRR abs/2002.11404 (2020) - [i14]Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab:
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks. CoRR abs/2003.10751 (2020) - [i13]Seong Tae Kim, Farrukh Mushtaq, Nassir Navab:
Confident Coreset for Active Learning in Medical Image Analysis. CoRR abs/2004.02200 (2020) - [i12]Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim, Nassir Navab:
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation. CoRR abs/2004.03675 (2020) - [i11]Hong Joo Lee, Seong Tae Kim, Hakmin Lee, Nassir Navab, Yong Man Ro:
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation. CoRR abs/2005.10754 (2020) - [i10]Hakmin Lee, Hong Joo Lee, Seong Tae Kim, Yong Man Ro:
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack. CoRR abs/2005.10757 (2020) - [i9]Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy, Franz Pfister, Nassir Navab:
Self-Supervised Out-of-Distribution Detection in Brain CT Scans. CoRR abs/2011.05428 (2020) - 2019
- [i8]Hyebin Lee, Seong Tae Kim, Yong Man Ro:
Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis. CoRR abs/1906.03922 (2019) - [i7]Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Seong Tae Kim, Nassir Navab:
Explaining Neural Networks via Perturbing Important Learned Features. CoRR abs/1911.11081 (2019) - 2018
- [i6]Seong Tae Kim, Hakmin Lee, Hak Gu Kim, Yong Man Ro:
ICADx: Interpretable computer aided diagnosis of breast masses. CoRR abs/1805.08960 (2018) - [i5]Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee, Yong Man Ro:
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation. CoRR abs/1809.06147 (2018) - 2017
- [i4]Seong Tae Kim, Yong Man Ro:
EvaluationNet: Can Human Skill be Evaluated by Deep Networks? CoRR abs/1705.11077 (2017) - [i3]Tae Kwan Lee, Wissam J. Baddar, Seong Tae Kim, Yong Man Ro:
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN. CoRR abs/1707.09855 (2017) - [i2]Geonmo Gu, Seong Tae Kim, Ki Hyun Kim, Wissam J. Baddar, Yong Man Ro:
Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data. CoRR abs/1711.10267 (2017) - [i1]Seong Tae Kim, Yong Man Ro:
Interpretable Facial Relational Network Using Relational Importance. CoRR abs/1711.10688 (2017)
Coauthor Index
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