8 citations · 18 across the 3 of their papers we have counts for
3 papers
cs.CV2021★ 8 cited
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Xiaofeng Liu, Xiongchang Liu, Bo Hu +7
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the…
cs.CV2021★ 3 cited
Energy-constrained Self-training for Unsupervised Domain Adaptation
Xiaofeng Liu, Bo Hu, Xiongchang Liu +3
Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain. Recently, the deep self-t…
cs.CV2020★ 7 cited
Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving
Xiaofeng Liu, Yimeng Zhang, Xiongchang Liu +3
Semantic segmentation is important for many real-world systems, e.g., autonomous vehicles, which predict the class of each pixel. Recently, deep networks achieved significant progr…