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Yibing Song

47 papers hereh-index 3910.8k citations75 works total

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

author position
  • first author6
  • middle author41

Across the 47 of 47 papers where every author was matched, so the position is known.

fields
  • cs.CV45
  • cs.LG1
  • math.CO1
same name
  • Yibing Song — 6 papers, h 7
  • Yibing Song — 4 papers, h 4
  • Yibing Song — 3 papers, h 2
  • Yibing Song — 3 papers, h 2
  • Yibing Song — 2 papers, h 2
  • Yibing Song — 2 papers, h 1

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
20132023
most citedVideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

441 citations · 1.5k across the 47 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 40 cited

Stylizing Face Images via Multiple Exemplars

Yibing Song, Linchao Bao, Shengfeng He +2

We address the problem of transferring the style of a headshot photo to face images. Existing methods using a single exemplar lead to inaccurate results when the exemplar does not…

cs.CV2017★ 68 cited

CREST: Convolutional Residual Learning for Visual Tracking

Yibing Song, Chao Ma, Lijun Gong +3

Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generat…

cs.CV2017★ 6 cited

Fast Preprocessing for Robust Face Sketch Synthesis

Yibing Song, Jiawei Zhang, Linchao Bao +1

Exemplar-based face sketch synthesis methods usually meet the challenging problem that input photos are captured in different lighting conditions from training photos. The critical…

cs.CV2017★ 21 cited

Learning to Hallucinate Face Images via Component Generation and Enhancement

Yibing Song, Jiawei Zhang, Shengfeng He +2

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Se…

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