32 citations · 62 across the 6 of their papers we have counts for
10 papers
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation
Byeongjun Park, Taekyung Kim, Hyojun Go +1
Photometric consistency loss is one of the representative objective functions commonly used for self-supervised monocular depth estimation. However, this loss often causes unstable…
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Seokeon Choi, Taekyung Kim, Minki Jeong +2
Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, gene…
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…
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
Seokeon Choi, Sumin Lee, Youngeun Kim +2
Visible-infrared person re-identification (VI-ReID) is an important task in night-time surveillance applications, since visible cameras are difficult to capture valid appearance in…
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…