PUBLICATIONS - Vision & Machine learning Lab

Dongjoo Kim and Minsik Lee, “Interpreting pretext tasks for active learning: a reinforcement learning approach,” Scientific Reports, vol. 14, pp. 25774, Oct. 2024. [paper]
Sangyoung Park, Changho No, Sora Kim, Kyoungmin Han, Jin-Man Jung, Kyum-Yil Kwon, and Minsik Lee, “A Multimodal Screening System for Elderly Neurological Diseases Based on Deep Learning,” Scientific Reports, vol. 13, pp. 21013, Nov. 2023. [paper]
Taeho Lee, Eun-Tae Jeon, Jin-Man Jung, and Minsik Lee, “Deep-Learning-Based Stroke Screening Using Skeleton Data from Neurological Examination Videos,” J. Personalized Medicine, vol. 12, no. 10, pp. 1691, Oct. 2022. [paper]
Kyoungmin Han, Kyujin Jung, Jaeho Yoon, and Minsik Lee, “Point Cloud Resampling by Simulating Electric Charges on Metallic Surfaces,” Sensors, vol. 21, no. 22, pp. 7768, Nov. 2021. [paper]
Younghan Jeon, Minsik Lee, and Jin Young Choi, “Differentiable Forward and Backward Fixed-Point Iteration Layers,” IEEE Access, vol. 9, pp. 18383 – 18392, Feb. 2021. [paper]
Geonho Cha, Minsik Lee, Jungchan Cho, and Songhwai Oh, “Reconstruct as Far as You Can: Consensus of Non-Rigid Reconstruction from Feasible Regions,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 43, no. 2, pp. 623 – 637, Feb. 2021. [paper]
Sora Kim, Youngjae Jo, Jungchan cho, Jiwoo Song, Younyoung Lee, and Minsik Lee, “Spatially Variant Convolutional Autoencoder Based on Patch Division for Pill Defect Detection,” IEEE Access, vol. 8, pp. 216781-216792, Dec. 2020. [paper]
Sungheon Park, Minsik Lee, and Nojun Kwak, “Procrustean Regression Networks: Learning 3D Structure of Non-Rigid Objects from 2D Annotations,” European Conf. Computer Vision (ECCV), Aug. 2020. [paper]
Eunwoo Kim, Minsik Lee, and Songhwai Oh, “Nonconvex Sparse Representation with Slowly Vanishing Gradient Regularizers,” IEEE Access, vol. 8, pp. 132489-132501, Jul. 2020. [paper]
Seunggyu Chang, Chanho Ahn, Minsik Lee, and Songhwai Oh, “Graph-Matching-Based Correspondence Search for Nonrigid Point Cloud Registration,” Computer Vision and Image Understanding, vol. 192, pp. 102899, Mar. 2020. [paper]
Jieun Lee, Hyeogjin Lee, Minsik Lee, and Nojun Kwak, “Nonparametric Estimation of Probabilistic Membership for Subspace Clustering,” IEEE Trans. Cybernetics, vol. 50, no. 3, pp. 1023-1036, Mar. 2020. [paper]
Moonsub Byeon, Minsik Lee, Kikyung Kim, and Jin Young Choi, “Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3260-3274, Nov. 2019. [paper]
Geonho Cha, Minsik Lee, and Songhwai Oh, “Unsupervised 3D Reconstruction Networks,” Int’l Conf. Computer Vision (ICCV), Oct. 2019. [paper]
Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, and Jin Young Choi, “Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning,” Int’l Conf. Computer Vision (ICCV), Oct. 2019. [paper]
Jungchan Cho and Minsik Lee, “Building a Compact Convolutional Neural Network for Embedded Intelligent Sensor Systems Using Group Sparsity and Knowledge Distillation,” Sensors, vol. 19, no. 19, pp. 4307, Oct. 2019. [paper]
Eunwoo Kim, Minsik Lee, and Songhwai Oh, “A Scalable Framework for Data-Driven Subspace Representation and Clustering,” Pattern Recognition Letters, vol. 125, pp. 742-749, Jul. 2019. [paper]
Minsik Lee, “Non-alternating stochastic K-means based on probabilistic representation of solution space,” IET Electronics Letters, vol. 55, no. 10, pp. 605-607, May 2019. [paper]
Geonho Cha, Minsik Lee, Jungchan Cho, and Songhwai Oh, “Deep Pose Consensus Networks,” Computer Vision and Image Understanding, vol. 182, pp. 64-70, May 2019. [paper]
Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi, “Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons,” AAAI Conf. Artificial Intelligence (AAAI), Jan. 2019. [paper]
Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi, “Knowledge Distillation with Adversarial Samples Supporting Decision Boundary,” AAAI Conf. Artificial Intelligence (AAAI), Jan. 2019. [paper]
Sang-Il Choi, Yonggeol Lee, and Minsik Lee, “Face Recognition in SSPP Problem Using Face Relighting Based on Coupled Bilinear Model,” Sensors, vol. 19, no. 1, pp. 43, Jan. 2019. [paper]
Geonho Cha, Minsik Lee, Jungchan Cho, and Songhwai Oh, “Non-Rigid Surface Recovery with a Robust Local-Rigidity Prior,” Pattern Recognition Letters, vol. 110, pp. 51-57, Jul. 2018. [paper]
Sungheon Park, Minsik Lee, and Nojun Kwak, “Procrustean Regression: A Flexible Alignment-Based Framework for Nonrigid Structure Estimation”, IEEE Trans. Image Processing, vol. 27, no. 1, pp. 249-264, Jan. 2018. [paper | project page]
Minsik Lee, Jungchan Cho, and Songhwai Oh, “Procrustean Normal Distribution for Non-Rigid Structure from Motion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 39, no. 7, pp. 1388-1400, July 2017. [paper | project page]
Jungchan Cho, Minsik Lee, and Songhwai Oh, “Single Image 3D Human Pose Estimation Using a Procrustean Normal Distribution Mixture Model and Model Transformation,” Computer Vision and Image Understanding, vol. 155, pp. 150–161, Feb. 2017. [paper]
Eunwoo Kim, Minsik Lee, and Songhwai Oh, “Robust Elastic-Net Subspace Representation,” IEEE Trans. Image Processing, vol. 25, no. 9, pp. 4245-4259, Sep. 2016. [paper]
Minsik Lee, Jungchan Cho, and Songhwai Oh, “Consensus of Non-Rigid Reconstructions,” IEEE Conf. Computer Vision and Pattern Recognition (CVPR), June 2016. (ORAL presentation, 3.9% acceptance rate) [paper | project page]
Jungchan Cho, Minsik Lee, and Songhwai Oh, “Complex Non-rigid 3D Shape Recovery Using a Procrustean Normal Distribution Mixture Model,” Int’l J. Computer Vision, vol. 117, no. 3, pp. 226-246, May 2016. [paper | project page]
Yonggeol Lee, Minsik Lee, and Sang-Il Choi, “Image Generation Using Bidirectional integral Features for Face Recognition with a Single Sample per Person,” PLOS ONE, vol. 10, no. 9, pp. e0138859, Sep. 2015. [paper]
Eunwoo Kim, Minsik Lee, and Songhwai Oh, “Elastic-net regularization of singular values for robust subspace learning,” IEEE Conf. Computer Vision and Pattern Recognition (CVPR), June 2015. [paper | software]
Minsik Lee, Jieun Lee, Hyeogjin Lee, and Nojun Kwak, “Membership representation for detecting block-diagonal structure in low-rank or sparse subspace clustering,” IEEE Conf. Computer Vision and Pattern Recognition (CVPR), June 2015. [paper | poster | supplementary]
Eunwoo Kim, Minsik Lee, Chong-Ho Choi, Nojun Kwak, and Songhwai Oh, “Efficient l_1-Norm-based Low-Rank Matrix Approximations for Large-Scale Problems Using Alternating Rectified Gradient Method,” IEEE Trans. Neural Networks and Learning Systems, vol. 26, no. 2, pp. 237-251, Feb. 2015. [paper]
Minsik Lee and Chong-Ho Choi, “Incremental N-mode SVD for Large-Scale Multilinear Generative Models,” IEEE Trans. Image Processing, vol. 23, no. 10, pp. 4255-4269, Oct. 2014. [paper]
Minsik Lee, Chong-Ho Choi, and Songhwai Oh, “A Procrustean Markov Process for Non-Rigid Structure Recovery,” IEEE Conf. Computer Vision and Pattern Recognition (CVPR), June 2014. [paper | project page]
Phuc Huu Truong, Chang-Woo Park, Minsik Lee, Sang-Il Choi, Sang-Hoon Ji, and Gu-Min Jeong, “Rapid Implementation of 3D Facial Reconstruction from a Single Image on an Android Mobile Device,” TIIS, vol. 8, no. 5, pp. 1690-1710, May 2014. [paper]
Jungchan Cho, Minsik Lee, Hyung Jin Chang, and Songhwai Oh, “Robust Action Recognition Using Local Motion and Group Sparsity,” Pattern Recognition, vol. 47, no. 5, pp. 1813-1825, May 2014. [paper]
Minsik Lee and Chong-Ho Choi, “Real-time Facial Shape Recovery From a Single Image under General, Unknown Lighting by Rank Relaxation,” Computer Vision and Image Understanding, vol. 120, pp. 59-69, Mar. 2014. [paper | project page]
Jiyong Oh, Nojun Kwak, Minsik Lee, and Chong-Ho Choi, “Generalized Mean for Feature Extraction in One-class Classification Problems,” Pattern Recognition, vol. 46, no. 12, pp. 3328-3340, Dec. 2013. [paper]
Jungchan Cho, Minsik Lee, and Chong-Ho Choi, and Songhwai Oh, “EM-GPA: Generalized Procrustes Analysis with Hidden Variables for a 3D Shape Model,” Computer Vision and Image Understanding, vol. 117, no. 11, pp. 1549-1559, Nov. 2013. [paper]
Youngsoo Lee, Minsik Lee, and Chong-Ho Choi, “Arbitration Interframe Space-controlled Medium Access Control: a Medium Access Control Protocol Guaranteeing Absolute Priority in Wireless Local Area Network,” Int’l J. Communication Systems, vol. 26, no. 7, pp. 832-852, Jul. 2013. [paper]
Minsik Lee, Jungchan Cho, Chong-Ho Choi, and Songhwai Oh, “Procrustean Normal Distribution for Non-Rigid Structure from Motion,” IEEE Conf. Computer Vision and Pattern Recognition (CVPR), June 2013. (ORAL presentation, 3.2% acceptance rate) [paper | project page]
Sungho Suh, Minsik Lee, and Chong-Ho Choi, “Robust Albedo Estimation from a Facial Image with Cast Shadow under General, Unknown Lighting,” IEEE Trans. Image Processing, vol. 22, no. 1, pp. 391-401, Jan. 2013. [paper]
Minsik Lee and Chong-Ho Choi, “A Robust Real-Time Algorithm for Facial Shape Recovery From a Single Image Containing Cast Shadow Under General, Unknown Lighting,” Pattern Recognition, vol. 46, no. 1, pp. 38-44, Jan. 2013. [paper | project page]
Sungho Suh, Minsik Lee, and Chong-Ho Choi, “Robust Albedo Estimation from a Facial Image with Cast Shadow,” IEEE int’l Conf. Image Processing (ICIP), Sep. 2011. [paper]
Minsik Lee and Chong-Ho Choi, “Fast Facial Shape Recovery From a Single Image With General, Unknown Lighting by Using Tensor Representation,” Pattern Recognition, vol. 44, no. 7, pp. 1487-1496, Jul. 2011. [paper | project page]
Minsik Lee, Youngjip Kim, and Chong-Ho Choi, “Period-Controlled MAC for High Performance in Wireless Networks,” IEEE/ACM Trans. Networking, vol. 19, no. 4, pp. 1237-1250, Aug. 2011. [paper]
Minsik Lee and Chong-Ho Choi, “Facial Shape Recovery From a Single Image With an Arbitrary Directional Light Using Linearly Independent Representation,” Int’l Symp. Visual Computing (ISVC), Nov. 2009. [paper]