34 citations · 65 across the 22 of their papers we have counts for
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cs.CV2024
MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation
Linyan Yang, Lukas Hoyer, Mark Weber +6
Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e.g., synthetic data, and an unlabeled target domain. We observe that c…
cs.CV2024
Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets
Wolfgang Boettcher, Lukas Hoyer, Ozan Unal +2
In this work, we introduce Scribbles for All, a label and training data generation algorithm for semantic segmentation trained on scribble labels. Training or fine-tuning semantic…