International Publications

Mitigating Search Interference with Task-Aware Nested Search
Jiho Lee and Eunwoo Kim
IEEE Transactions on Image Processing (TIP), vol. 33, pp. 3102-3114, Apr. 2024.

Gravitated Latent Space Loss Generated by Metric Tensor for High-Dynamic Range Imaging
Heunseung Lim, Jungkyoo Shin, Hyoungki Choi, Dohoon Kim, Eunwoo Kim, and Joonki Paik
In Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Apr. 2024.

Task-Aware Dynamic Model Optimization for Multi-Task Learning
Sujin Choi, Hyundong Jin, and Eunwoo Kim
IEEE Access, vol. 11, pp. 137709-137717, Dec. 2023.

Growing a Brain with Sparsity-Inducing Generation for Continual Learning
Hyundong Jin, Gyeong-Hyeon Kim, Chanho Ahn, and Eunwoo Kim
In Proc. of the IEEE International Conference on Computer Vision (ICCV), Oct. 2023.

GhostNeXt: Rethinking Module Configurations for Efficient Model Design
Kiseong Hong, Gyeong-Hyeon Kim, and Eunwoo Kim
Applied Sciences, vol. 13, no. 5, Mar. 2023.

Stacked Encoder-Decoder Transformer with Boundary Smoothing for Action Segmentation
Gyeong-Hyeon Kim and Eunwoo Kim
Electronics Letters, vol. 58, no. 25, pp. 972-974, Dec. 2022.

Helpful or Harmful: Inter-Task Association in Continual Learning
Hyundong Jin and Eunwoo Kim
In Proc. of the European Conference on Computer Vision (ECCV), Oct. 2022.

Resource-Efficient Multi-Task Deep Learning Using a Multi-Path Network
Soyeon Park, Jiho Lee, and Eunwoo Kim
IEEE Access, vol. 10, pp. 32889-32899, Mar. 2022.

Improving Augmentation Efficiency for Few-Shot Learning
Wonhee Cho and Eunwoo Kim
IEEE Access, vol. 10, pp. 17697-17706, Feb. 2022.

Incremental Learning with Adaptive Model Search and a Nominal Loss Model
Chanho Ahn, Eunwoo Kim, and Songhwai Oh
IEEE Access, vol. 10, pp. 16052-16062, Feb. 2022.

Gating Mechanism in Deep Neural Networks for Resource-Efficient Continual Learning
Hyundong Jin, Kimin Yun, and Eunwoo Kim
IEEE Access, vol. 10, pp. 18776-18786, Jan. 2022.

Auto-VirtualNet: Cost-Adaptive Dynamic Architecture Search for Multi-Task Learning
Eunwoo Kim, Chanho Ahn, and Songhwai Oh
Neurocomputing, vol. 442, pp. 116-124, June 2021.

Clustering-Guided Incremental Learning of Tasks
Yoonhee Kim and Eunwoo Kim
In Proc. of the IEEE International Conference on Information Networking, Jan. 2021.

Nonconvex Sparse Representation with Slowly Vanishing Gradient Regularizers
Eunwoo Kim, Minsik Lee, and Songhwai Oh
IEEE Access, vol. 8, pp. 132489-132501, July 2020.

Deep Elastic Networks with Model Selection for Multi-Task Learning
Chanho Ahn*, Eunwoo Kim*, and Songhwai Oh (* equal contribution)
In Proc. of the IEEE International Conference on Computer Vision (ICCV), Oct. 2019.

A Scalable Framework for Data-Driven Subspace Representation and Clustering
Eunwoo Kim, Minsik Lee, and Songhwai Oh
Pattern Recognition Letters, vol. 125, pp. 742-749, July 2019.

Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
Eunwoo Kim, Chanho Ahn, Philip H.S. Torr, and Songhwai Oh
In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.

NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
Eunwoo Kim, Chanho Ahn, and Songhwai Oh
In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
(Spotlight Presentation)

Structured Kernel Subspace Learning for Autonomous Robot Navigation
Eunwoo Kim, Sungjoon Choi, and Songhwai Oh
Sensors, vol. 18, no. 2, Feb. 2018.

Real-Time Nonparametric Reactive Navigation of Mobile Robots in Dynamic Environments
Sungjoon Choi, Eunwoo Kim, Kyungjae Lee, and Songhwai Oh
Robotics and Autonomous Systems, vol. 91, pp. 11-24, May 2017.

Robust Elastic-Net Subspace Representation
Eunwoo Kim, Minsik Lee, and Songhwai Oh
IEEE Transactions on Image Processing (TIP), vol. 25, no. 9, pp. 4245-4259, Sep. 2016.

Robust Orthogonal Matrix Factorization for Efficient Subspace Learning
Eunwoo Kim and Songhwai Oh
Neurocomputing, vol. 167, pp. 218-229, Nov. 2015.

Elastic-Net Regularization of Singular Values for Robust Subspace Learning
Eunwoo Kim, Minsik Lee, and Songhwai Oh
In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.

Leveraged Non-Stationary Gaussian Process Regression for Autonomous Robot Navigation
Sungjoon Choi, Eunwoo Kim, and Songhwai Oh
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), May 2015.

Structured Low-Rank Matrix Approximation in Gaussian Process Regression for Autonomous Robot Navigation
Eunwoo Kim, Sungjoon Choi, and Songhwai Oh
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), May 2015.

Efficient l1-Norm-Based Low-Rank Matrix Approximations for Large-Scale Problems Using Alternating Rectified Gradient Method
Eunwoo Kim, Minsik Lee, Chong-Ho Choi, Nojun Kwak, and Songhwai Oh
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 26, no. 2, pp. 237-251, Feb. 2015.

A Robust Autoregressive Gaussian Process Motion Model Using l1-Norm Based Low-Rank Kernel Matrix Approximation
Eunwoo Kim, Sungjoon Choi, and Songhwai Oh
In Proc. of the IEEE International Conference on Intelligent Robots and Systems (IROS), Sep. 2014.

Real-Time Navigation in Crowded Dynamic Environments Using Gaussian Process Motion Control
Sungjoon Choi, Eunwoo Kim, and Songhwai Oh
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), May 2014.

Human Behavior Prediction for Smart Homes Using Deep Learning
Sungjoon Choi, Eunwoo Kim, and Songhwai Oh
In Proc. of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Aug. 2013.