activity
20172026
most citedDeep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

34 citations · 213 across the 28 of their papers we have counts for

collaborators
Showing 2021Show all

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.CV2021

Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

Yubin Ge, Site Li, Xuyang Li +4

The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk o…

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★ 2 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.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…