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20192021
most citedSubtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

8 citations · 18 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CV2021

Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation

Xiaofeng Liu, Site Li, Yubin Ge +3

The unsupervised domain adaptation (UDA) has been widely adopted to alleviate the data scalability issue, while the existing works usually focus on classifying independently discre…

cs.CV20218 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.CV20212 cited

Identity-aware Facial Expression Recognition in Compressed Video

Xiaofeng Liu, Linghao Jin, Xu Han +3

This paper targets to explore the inter-subject variations eliminated facial expression representation in the compressed video domain. Most of the previous methods process the RGB…

cs.CV20213 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

Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training

Xiaofeng Liu, Yuzhuo Han, Song Bai +6

Semantic segmentation (SS) is an important perception manner for self-driving cars and robotics, which classifies each pixel into a pre-determined class. The widely-used cross entr…