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Found 6 papers, 2 papers with code
Towards Uniform Point Distribution in Feature-preserving Point Cloud Filtering
As a popular representation of 3D data, point cloud may contain noise and need to be filtered before use.
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.
Ranked #2 on
Unsupervised Domain Adaptation
on PACS
Multi-Target Domain Adaptation with Collaborative Consistency Learning
To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.
Ranked #4 on
Domain Adaptation
on GTAV to Cityscapes+Mapillary
Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation
Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain.
Ranked #1 on
Domain Adaptation
on GTA5+Synscapes to Cityscapes
Multi-Source Unsupervised Domain Adaptation
Semantic Segmentation
+1
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed.
Unsupervised Image Super-Resolution with an Indirect Supervised Path
The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image.
