most citedPoint Cloud Upsampling via Cascaded Refinement Network

7 citations · 14 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV20221 cited

FBNet: Feedback Network for Point Cloud Completion

Xuejun Yan, Hongyu Yan, Jingjing Wang +5

The rapid development of point cloud learning has driven point cloud completion into a new era. However, the information flows of most existing completion methods are solely feedfo…

cs.CV20227 cited

Point Cloud Upsampling via Cascaded Refinement Network

Hang Du, Xuejun Yan, Jingjing Wang +2

Point cloud upsampling focuses on generating a dense, uniform and proximity-to-surface point set. Most previous approaches accomplish these objectives by carefully designing a sing…

cs.CV20221 cited

Multi-Scale Wavelet Transformer for Face Forgery Detection

Jie Liu, Jingjing Wang, Peng Zhang +3

Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability and gain promising performance under the cross-dataset…

cs.CV2022

Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition

Jingwei Yan, Jingjing Wang, Qiang Li +2

Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicat…

cs.CV2022

Few-shot One-class Domain Adaptation Based on Frequency for Iris Presentation Attack Detection

Yachun Li, Ying Lian, Jingjing Wang +3

Iris presentation attack detection (PAD) has achieved remarkable success to ensure the reliability and security of iris recognition systems. Most existing methods exploit discrimin…

cs.CV20221 cited

Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression

Qiang Li, Jingjing Wang, Zhaoliang Yao +5

Learning from a label distribution has achieved promising results on ordinal regression tasks such as facial age and head pose estimation wherein, the concept of adaptive label dis…