Search Results for author: Jonghwa Yim

Found 6 papers, 0 papers with code

StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map

no code implementations13 May 2022 GunHee Lee, Jonghwa Yim, Chanran Kim, Minjae Kim

Despite recent success in conditional image synthesis, prevalent input conditions such as semantics and edges are not clear enough to express `Linear (Ridges)' and `Planar (Scale)' representations.

Image Generation

Learning Boost by Exploiting the Auxiliary Task in Multi-task Domain

no code implementations5 Aug 2020 Jonghwa Yim, Sang Hwan Kim

While MTL benefits from the positive transfer of information from multiple tasks, in a real environment, tasks inevitably have a conflict between them during the learning phase, called negative transfer.

Multi-Task Learning Speech Synthesis

Filter Style Transfer between Photos

no code implementations ECCV 2020 Jonghwa Yim, Jisung Yoo, Won-joon Do, Beomsu Kim, Jihwan Choe

Unlike conventional style transfer, new technique FST can extract and transfer custom filter style from a filtered style image to a content image.

Image-to-Image Translation Style Transfer +1

One-Shot Item Search with Multimodal Data

no code implementations27 Nov 2018 Jonghwa Yim, Junghun James Kim, Daekyu Shin

However, since deep neural network emerged, the performance of visual search becomes high enough to apply it in many industries from 3D data to multimodal data.

Image Retrieval Text Matching

Investigating the feature collection for semantic segmentation via single skip connection

no code implementations23 Oct 2017 Jonghwa Yim, Kyung-Ah Sohn

Therefore, in this study, we exhaustively research skip connections of state-of-the-art deep convolutional networks and investigate the characteristics of the features from each intermediate layer.

Object Detection Semantic Segmentation

Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets

no code implementations18 Oct 2017 Jonghwa Yim, Kyung-Ah Sohn

Despite the appeal of deep neural networks that largely replace the traditional handmade filters, they still suffer from isolated cases that cannot be properly handled only by the training of convolutional filters.

Image Classification

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