Search Results for author: Jisoo Jeong

Found 9 papers, 5 papers with code

Deep Learning-based High-precision Depth Map Estimation from Missing Viewpoints for 360 Degree Digital Holography

no code implementations9 Mar 2021 Hakdong Kim, Heonyeong Lim, Minkyu Jee, Yurim Lee, Jisoo Jeong, Kyudam Choi, MinSung Yoon, Cheongwon Kim

In this paper, we propose a novel, convolutional neural network model to extract highly precise depth maps from missing viewpoints, especially well applicable to generate holographic 3D contents.

Interpolation-based semi-supervised learning for object detection

1 code implementation CVPR 2021 Jisoo Jeong, Vikas Verma, Minsung Hyun, Juho Kannala, Nojun Kwak

Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much.

Object Detection

Class-Imbalanced Semi-Supervised Learning

1 code implementation17 Feb 2020 Minsung Hyun, Jisoo Jeong, Nojun Kwak

First, we analyze existing SSL methods in imbalanced environments and examine how the class imbalance affects SSL methods.

Selective Self-Training for semi-supervised Learning

no code implementations ICLR 2019 Jisoo Jeong, Seungeui Lee, Nojun Kwak

While the conventional methods cannot be applied to the new SSL problems where the separated data do not share the classes, our method does not show any performance degradation even if the classes of unlabeled data are different from those of the labeled data.

Residual Features and Unified Prediction Network for Single Stage Detection

1 code implementation17 Jul 2017 Kyoungmin Lee, Jae-Seok Choi, Jisoo Jeong, Nojun Kwak

They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances.

Region Proposal

Superpixel-based Semantic Segmentation Trained by Statistical Process Control

1 code implementation30 Jun 2017 Hyojin Park, Jisoo Jeong, Youngjoon Yoo, Nojun Kwak

Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.

Semantic Segmentation

Enhancement of SSD by concatenating feature maps for object detection

no code implementations26 May 2017 Jisoo Jeong, Hyojin Park, Nojun Kwak

In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD.

Object Detection

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