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20202022
most citedPoint Cloud Upsampling via Cascaded Refinement Network

7 citations · 10 across the 5 of their papers we have counts for

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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.CV2022

Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation

Hailin Shi, Hang Du, Yibo Hu +3

Human face images usually appear with wide range of visual scales. The existing face representations pursue the bandwidth of handling scale variation via multi-scale scheme that as…

cs.CV2021

Boosting Semi-Supervised Face Recognition with Noise Robustness

Yuchi Liu, Hailin Shi, Hang Du +4

Although deep face recognition benefits significantly from large-scale training data, a current bottleneck is the labelling cost. A feasible solution to this problem is semi-superv…

cs.CV2021

Towards NIR-VIS Masked Face Recognition

Hang Du, Hailin Shi, Yinglu Liu +2

Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different…

cs.CV20201 cited

Scene Text Detection with Selected Anchor

Anna Zhu, Hang Du, Shengwu Xiong

Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy…