21 citations · 29 across the 7 of their papers we have counts for
7 papers
Arbitrary Style Transfer using Graph Instance Normalization
Dongki Jung, Seunghan Yang, Jaehoon Choi +1
Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization…
Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation
Taekyung Kim, Changick Kim
Although unsupervised domain adaptation methods have been widely adopted across several computer vision tasks, it is more desirable if we can exploit a few labeled data from new do…
Partial Domain Adaptation Using Graph Convolutional Networks
Seunghan Yang, Youngeun Kim, Dongki Jung +1
Partial domain adaptation (PDA), in which we assume the target label space is included in the source label space, is a general version of standard domain adaptation. Since the targ…
BitNet: Learning-Based Bit-Depth Expansion
Junyoung Byun, Kyujin Shim, Changick Kim
Bit-depth is the number of bits for each color channel of a pixel in an image. Although many modern displays support unprecedented higher bit-depth to show more realistic and natur…
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation
Youngeun Kim, Seokeon Choi, Hankyeol Lee +2
In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data.Specifically, we present Robust Pixel-level Matching Net-works (RPM-…
CNN-based Semantic Segmentation using Level Set Loss
Youngeun Kim, Seunghyeon Kim, Taekyung Kim +1
Thesedays, Convolutional Neural Networks are widely used in semantic segmentation. However, since CNN-based segmentation networks produce low-resolution outputs with rich semantic…