18 citations · 23 across the 4 of their papers we have counts for
4 papers
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model
Lingsheng Kong, Bo Hu, Xiongchang Liu +3
Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target dom…
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…
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…
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition
Xiaofeng Liu, Zhenhua Guo, Site Li +4
We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images. We use feature restructuring to exploit the c…