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Xiangyu Zhu

73 papers hereh-index 338k citations136 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author64
  • last author1

Across the 70 of 73 papers where every author was matched, so the position is known.

fields
  • cs.CV66
  • cs.CL3
  • cs.CE2
  • cs.GR1
  • eess.IV1
same name
  • Xiangyu Zhu — 11 papers, h 3
  • Xiangyu Zhu — 6 papers, h 7
  • Xiangyu Zhu — 5 papers, h 3
  • Xiangyu Zhu — 4 papers, h 7
  • Xiangyu Zhu — 4 papers, h 6
  • Xiangyu Zhu — 3 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152026
most citedFace Alignment Across Large Poses: A 3D Solution

406 citations · 1.6k across the 71 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CV2019★ 5 cited

Grand Challenge of 106-Point Facial Landmark Localization

Yinglu Liu, Hao Shen, Yue Si +18

Facial landmark localization is a very crucial step in numerous face related applications, such as face recognition, facial pose estimation, face image synthesis, etc. However, pre…

cs.CV2019★ 102 cited

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu +6

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various pre…

cs.CV2019★ 3 cited

Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detection

Zhiwei Liu, Xiangyu Zhu, Guosheng Hu +5

Recently, deep learning based facial landmark detection has achieved great success. Despite this, we notice that the semantic ambiguity greatly degrades the detection performance.…

cs.CV2019★ 6 cited

Improving Face Anti-Spoofing by 3D Virtual Synthesis

Jianzhu Guo, Xiangyu Zhu, Jinchuan Xiao +3

Face anti-spoofing is crucial for the security of face recognition systems. Learning based methods especially deep learning based methods need large-scale training samples to reduc…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.