57 citations · 63 across the 5 of their papers we have counts for
5 papers
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Taeyeop Lee, Jonathan Tremblay, Valts Blukis +6
Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requir…
Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion
Ukcheol Shin, Kyunghyun Lee, Byeong-Uk Lee +1
Recently, self-supervised learning of depth and ego-motion from thermal images shows strong robustness and reliability under challenging scenarios. However, the inherent thermal im…
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Taeyeop Lee, Byeong-Uk Lee, Inkyu Shin +4
Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real w…
Category-Level Metric Scale Object Shape and Pose Estimation
Taeyeop Lee, Byeong-Uk Lee, Myungchul Kim +1
Advances in deep learning recognition have led to accurate object detection with 2D images. However, these 2D perception methods are insufficient for complete 3D world information.…
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Kyunghyun Lee, Byeong-Uk Lee, Ukcheol Shin +1
Deep reinforcement learning (DRL) algorithms and evolution strategies (ES) have been applied to various tasks, showing excellent performances. These have the opposite properties, w…