most citedECAP: Extensive Cut-and-Paste Augmentation for Unsupervised Domain Adaptive Semantic Segmentation

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CV2024

MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian Detection

Erik Brorsson, Lennart Svensson, Kristofer Bengtsson +1

We address multi-view pedestrian detection in a setting where labeled data is collected using a multi-camera setup different from the one used for testing. While recent multi-view…

cs.CV2024★ 3 cited

ECAP: Extensive Cut-and-Paste Augmentation for Unsupervised Domain Adaptive Semantic Segmentation

Erik Brorsson, Knut Åkesson, Lennart Svensson +1

We consider unsupervised domain adaptation (UDA) for semantic segmentation in which the model is trained on a labeled source dataset and adapted to an unlabeled target dataset. Unf…