activity
20182021
most citedDouble Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion

11 citations · 29 across the 6 of their papers we have counts for

collaborators

10 papers

cs.CV2021

Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing

Zhihong Chen, Taiping Yao, Kekai Sheng +5

Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios. Existing DG methods assume that the do-main la…

cs.CV20211 cited

DCANet: Dense Context-Aware Network for Semantic Segmentation

Yifu Liu, Chenfeng Xu, Xinyu Jin

As the superiority of context information gradually manifests in advanced semantic segmentation, learning to capture the compact context relationship can help to understand the com…

cs.CV20203 cited

Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment

Chao Chen, Zhihong Chen, Xinyu Jin +3

Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine and metabolic disorders during child development. Existing deep learning based methods for…

cs.CV20197 cited

HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation

Chao Chen, Zhihang Fu, Zhihong Chen +4

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of dis…

cs.LG2019

Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation

Zhihong Chen, Chao Chen, Zhaowei Cheng +3

One crucial aspect of partial domain adaptation (PDA) is how to select the relevant source samples in the shared classes for knowledge transfer. Previous PDA methods tackle this pr…

cs.CV20193 cited

Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation

Chao Chen, Zhihang Fu, Zhihong Chen +3

Recent advances in unsupervised domain adaptation mainly focus on learning shared representations by global distribution alignment without considering class information across doma…