Fayao Liu

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 727 researchers and is trusted by 858 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 129 citations into it; #1,500 of 21,575 by trust here, #2,536 by citations
• Artificial Intelligence — 61 citations into it; #7,037 of 35,345 by trust here, #8,210 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Media Technology — 16 citations into it; #476 of 3,621 by trust here, #529 by citations
• Model Architectures — 11 citations into it
• Aerospace Engineering — 10 citations into it
• Meta & Continual Learning — 10 citations into it; #913 of 1,041 by trust here, #660 by citations
• 3D Vision & Pose — 8 citations into it; #107 of 689 by trust here, #101 by citations

Whom Fayao Liu trusts overall

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

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

Who trusts Fayao Liu overall

1. Guosheng Lin
2. Chunhua Shen
3. Chi Zhang
4. Dan Xu
5. Ian David Reid
6. Mateusz Michalkiewicz
7. Zhengqi Li
8. Boyi Li
9. Yinda Zhang
10. Wanli Ouyang

Papers from Fayao Liu's perspective

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

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

Their papers

• Deep convolutional neural fields for depth estimation from a single image (2014) — 934 citations
• Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields (2015) — 1,273 citations
• CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning (2019) — 674 citations
• Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation (2019) — 358 citations
• CRF Learning with CNN Features for Image Segmentation (2015) — 217 citations
• CRNet: Cross-Reference Networks for Few-Shot Segmentation (2020) — 240 citations