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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 2019Show all

4 papers · 1 filter

cs.CV2019★ 63 cited

Self-supervised Learning of Detailed 3D Face Reconstruction

Yajing Chen, Fanzi Wu, Zeyu Wang +3

In this paper, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement m…

cs.CV2019★ 55 cited

Real-Time Correlation Tracking via Joint Model Compression and Transfer

Ning Wang, Wengang Zhou, Yibing Song +2

Correlation filters (CF) have received considerable attention in visual tracking because of their computational efficiency. Leveraging deep features via off-the-shelf CNN models (e…

cs.CV2019★ 7 cited

MVF-Net: Multi-View 3D Face Morphable Model Regression

Fanzi Wu, Linchao Bao, Yajing Chen +5

We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morpha…

cs.CV2019★ 31 cited

Unsupervised Deep Tracking

Ning Wang, Yibing Song, Chao Ma +3

We propose an unsupervised visual tracking method in this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN model is trained…

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