May 18, 2023 · Domain gap between synthetic and real data in visual regression (e.g. 6D pose estimation) is bridged in this paper via global feature alignment ...
[PDF] Manifold-Aware Self-Training for Unsupervised Domain Adaptation ...
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This paper proposes a novel and generic manifold-aware self- training scheme for UDA on regression, which is applied to the challenging 6D pose estimation of ...
[IJCAI 2023] Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose Installation - Gorilla-Lab-SCUT/MAST.
Domain gap between synthetic and real data in visual regression (e.g., 6D pose estimation) is bridged in this paper via global feature alignment and local ...
Nov 20, 2023 · Manifold-Aware Self-Training for Unsupervised Domain Adaptation on. Regressing 6D Object Pose. Yichen Zhang1 , Jiehong Lin1 , Ke Chen1,2 ...
May 18, 2023 · PDF | Domain gap between synthetic and real data in visual regression (\eg 6D pose estimation) is bridged in this paper via global feature ...
self-training to perform unsupervised domain adaptation [36,64], which train the teacher and student models on different domains. Recently, some methods ...
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This thesis introduces a number of deep learning-based solutions to this task with a focus on industrial usability. The first part presents pose estimation.
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@inproceedings{ijcai2023p193, title = {Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose}, author = {Zhang ...
Manifold-aware self-training for unsupervised domain adaptation on regressing 6D object pose. Y Zhang, J Lin, K Chen, Z Xu, Y Wang, K Jia. arXiv preprint ...