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Xiang Xiang

11 papers hereh-index 101.2k citations34 works total

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

author position
  • first author3
  • middle author5
  • last author2

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CV11
same name
  • Xiang Xiang — 5 papers, h 3
  • Xiang Xiang — 4 papers, h 2
  • Xiang Xiang — 3 papers, h 2
  • Xiang Xiang — 3 papers, h 2
  • Xiang Xiang — 2 papers, h 3
  • Xiang Xiang — 2 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
20142022
most citedNormFace: L2 Hypersphere Embedding for Face Verification

584 citations · 698 across the 11 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 10 cited

Adversarial Deep Structured Nets for Mass Segmentation from Mammograms

Wentao Zhu, Xiang Xiang, Trac D. Tran +2

Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmen…

cs.CV2017★ 584 cited

NormFace: L2 Hypersphere Embedding for Face Verification

Feng Wang, Xiang Xiang, Jian Cheng +1

Thanks to the recent developments of Convolutional Neural Networks, the performance of face verification methods has increased rapidly. In a typical face verification method, featu…

cs.CV2017★ 15 cited

Regularizing Face Verification Nets For Pain Intensity Regression

Feng Wang, Xiang Xiang, Chang Liu +6

Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is l…

cs.CV2017★ 2 cited

Linear Disentangled Representation Learning for Facial Actions

Xiang Xiang, Trac D. Tran

Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant feature…

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