23 citations · 36 across the 5 of their papers we have counts for
5 papers
Divide, Ensemble and Conquer: The Last Mile on Unsupervised Domain Adaptation for Semantic Segmentation
Tao Lian, Jose L. Gómez, Antonio M. López
The last mile of unsupervised domain adaptation (UDA) for semantic segmentation is the challenge of solving the syn-to-real domain gap. Recent UDA methods have progressed significa…
UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation
Yachan Guo, Yi Xiao, Danna Xue +2
Instance segmentation is crucial for autonomous driving, but is hindered by the lack of annotated real-world data due to expensive labeling costs. Unsupervised Domain Adaptation (U…
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
Jose L. Gómez, Gabriel Villalonga, Antonio M. López
Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image la…
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example
Antonio M. Lopez, Jiaolong Xu, Jose L. Gomez +2
Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing…
Very Long Baseline Interferometry with the SKA
Zsolt Paragi, Leith Godfrey, Cormac Reynolds +79
Adding VLBI capability to the SKA arrays will greatly broaden the science of the SKA, and is feasible within the current specifications. SKA-VLBI can be initially implemented by pr…