no code implementations • 22 May 2023 • Kyoungmin Han, Minsik Lee
Recently, contrastive self-supervised learning, where the proximity of representations is determined based on the identities of samples, has made remarkable progress in unsupervised representation learning.
no code implementations • ECCV 2020 • Sungheon Park, Minsik Lee, Nojun Kwak
We propose a novel framework for training neural networks which is capable of learning 3D information of non-rigid objects when only 2D annotations are available as ground truths.
no code implementations • 7 Feb 2020 • Younghan Jeon, Minsik Lee, Jin Young Choi
FPI\_NN is intuitive, simple, and fast to train, while FPI_GD can be used for efficient training of energy networks that have been recently studied.
1 code implementation • ICCV 2019 • Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, Jin Young Choi
For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture.
no code implementations • 24 May 2019 • Younghan Jeon, Minsik Lee, Jin Young Choi
With a carefully-designed objective function, mathematical optimization can be quite helpful in solving many problems.
2 code implementations • 8 Nov 2018 • Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi
In this paper, we propose a knowledge transfer method via distillation of activation boundaries formed by hidden neurons.
1 code implementation • 15 May 2018 • Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi
In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier.
no code implementations • 22 Mar 2018 • Geonho Cha, Minsik Lee, Jungchan Cho, Songhwai Oh
In this paper, to resolve this issue, we propose a multiple-partial-hypothesis-based framework for the problem of estimating 3D human pose from a single image, which can be fine-tuned in an end-to-end fashion.
no code implementations • CVPR 2016 • Minsik Lee, Jungchan Cho, Songhwai Oh
Recently, there have been many progresses for the problem of non-rigid structure reconstruction based on 2D trajectories, but it is still challenging to deal with complex deformations or restricted view ranges.
no code implementations • CVPR 2015 • Eunwoo Kim, Minsik Lee, Songhwai Oh
The proposed method is applied to a number of low-rank matrix approximation problems to demonstrate its efficiency in the presence of heavy corruptions and to show its effectiveness and robustness compared to the existing methods.
no code implementations • CVPR 2015 • Minsik Lee, Jieun Lee, Hyeogjin Lee, Nojun Kwak
The proposed method shares the philosophy of the above subspace clustering methods, in that it is a self-expressive system based on a Hadamard product of a membership matrix.
no code implementations • CVPR 2014 • Minsik Lee, Chong-Ho Choi, Songhwai Oh
Recovering a non-rigid 3D structure from a series of 2D observations is still a difficult problem to solve accurately.
no code implementations • CVPR 2013 • Minsik Lee, Jungchan Cho, Chong-Ho Choi, Songhwai Oh
Non-rigid structure from motion is a fundamental problem in computer vision, which is yet to be solved satisfactorily.