Zitong Yu

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 7 papers; trusts 482 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 171 citations into it; #18,550 of 21,575 by trust here, #13,815 by citations
• Artificial Intelligence — 55 citations into it
• Signal Processing — 53 citations into it
• Model Architectures — 39 citations into it
• Biomedical Engineering — 29 citations into it; #5,068 of 10,734 by trust here, #9,539 by citations
• Cardiology and Cardiovascular Medicine — 21 citations into it; #332 of 906 by trust here, #760 by citations
• Cognitive Neuroscience — 14 citations into it
• Media Technology — 11 citations into it

Whom Zitong Yu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Ross B. Girshick
6. Yoshua Bengio
7. Diederik P. Kingma
8. Andrew Zisserman
9. Yann LeCun
10. Ilya Sutskever

Who trusts Zitong Yu overall

1. Xuesong Niu
2. Zewei He
3. Xiaobai Li
4. Xin Liu
5. Liucheng Hu
6. Jingang Shi
7. Zhe-ming Lu
8. Guoying Zhao
9. Shiguang Shan
10. Josh Fromm

Papers from Zitong Yu's perspective

Ordered by how strongly the researchers Zitong Yu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Searching Central Difference Convolutional Networks for Face Anti-Spoofing (2020) — 569 citations
• Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks (2019) — 419 citations
• Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019) — 332 citations
• Pixel Difference Networks for Efficient Edge Detection (2021) — 527 citations
• PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer (2021) — 314 citations
• Face Anti-Spoofing with Human Material Perception (2020) — 155 citations
• Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020) — 215 citations