Search Results for author: Jeongwoo Ju

Found 5 papers, 1 papers with code

Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints

no code implementations19 Nov 2021 Jaesin Ahn, Jiuk Hong, Jeongwoo Ju, Heechul Jung

The proposed method achieved $71. 4\%$ with a few parameters (of $3. 1M$) on the ImageNet-1k dataset compared to that required by the original transformer model of XCiT-N12 ($69. 9\%$).

Image Classification Inductive Bias +1

Extending Contrastive Learning to Unsupervised Coreset Selection

1 code implementation5 Mar 2021 Jeongwoo Ju, Heechul Jung, Yoonju Oh, Junmo Kim

Self-supervised contrastive learning offers a means of learning informative features from a pool of unlabeled data.

Contrastive Learning

Less-forgetful Learning for Domain Expansion in Deep Neural Networks

no code implementations16 Nov 2017 Heechul Jung, Jeongwoo Ju, Minju Jung, Junmo Kim

Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned information when learning new data from a new domain.

Domain Adaptation Image Classification

Less-forgetting Learning in Deep Neural Networks

no code implementations1 Jul 2016 Heechul Jung, Jeongwoo Ju, Minju Jung, Junmo Kim

Surprisingly, our method is very effective to forget less of the information in the source domain, and we show the effectiveness of our method using several experiments.

Rigid Motion Segmentation using Randomized Voting

no code implementations CVPR 2014 Heechul Jung, Jeongwoo Ju, Junmo Kim

For evaluation of our algorithm, Hopkins 155 dataset, which is a representative test set for rigid motion segmentation, is adopted; it consists of two and three rigid motions.

Motion Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.