41 citations · 64 across the 22 of their papers we have counts for
4 papers · 1 filter
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…
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
KwanYong Park, Sanghyun Woo, Inkyu Shin +1
Unsupervised domain adaptation (UDA) for semantic segmentation has been attracting attention recently, as it could be beneficial for various label-scarce real-world scenarios (e.g.…
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
Inkyu Shin, Dong-jin Kim, Jae Won Cho +3
Unsupervised Domain Adaptation (UDA) for semantic segmentation has been actively studied to mitigate the domain gap between label-rich source data and unlabeled target data. Despit…
Unsupervised Domain Adaptation for Video Semantic Segmentation
Inkyu Shin, Kwanyong Park, Sanghyun Woo +1
Unsupervised Domain Adaptation for semantic segmentation has gained immense popularity since it can transfer knowledge from simulation to real (Sim2Real) by largely cutting out the…