Publications

Selected Publications (Full List of Publications)

Exploration and Exploitation in Continual Learning
Kiseong Hong, Hyundong Jin, Sungho Suh, and Eunwoo Kim
Neural Networks, vol. 188, Aug. 2025.

Generative Modeling of Class Probability for Multi Modal Representation Learning
Jungkyoo Shin, Bumsoo Kim, and Eunwoo Kim
In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2025.
(Highlight, Acceptance Rate: 3%)

Self-Corrective Task Planning by Inverse Prompting with Large Language Models
Jiho Lee, Hayun Lee, Jonghyeon Kim, Kyungjae Lee, and Eunwoo Kim
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), May 2025. [Video]

Task Planning for Long-Horizon Cooking Tasks Based on Large Language Models
Jungkyoo Shin, Jieun Han, Seungjun Kim, Yoonseon Oh, and Eunwoo Kim
In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2024. [Video]
(Oral Presentation)

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.

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.

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.

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.

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.

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)

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.