34 citations · 65 across the 22 of their papers we have counts for
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cs.CV2021★ 1 cited
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
Lukas Hoyer, Dengxin Dai, Luc Van Gool
As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapt…
cs.CV2021★ 2 cited
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Qin Wang +2
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
cs.CV2021★ 6 cited
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Qin Wang, Dengxin Dai, Lukas Hoyer +2
Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervisi…