Shan Li

Computer Vision and Pattern Recognition · with Deng · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 929 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 173 citations into it; #5,760 of 21,575 by trust here, #9,872 by citations
• Experimental and Cognitive Psychology — 78 citations into it; #1,216 of 9,731 by trust here, #3,093 by citations
• Artificial Intelligence — 50 citations into it
• Signal Processing — 25 citations into it
• Model Architectures — 23 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Cognitive Neuroscience — 18 citations into it
• Generative Models — 9 citations into it

Whom Shan Li trusts overall

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

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

Who trusts Shan Li overall

1. Kai-Hong Wang
2. Fuyan Ma
3. Zengqun Zhao
4. Radek Daněček
5. Dimitrios Kollias
6. M. Kalpana Chowdary
7. Yan Wang
8. Weihong Deng
9. Xiaojiang Peng
10. Yong Li

Papers from Shan Li's perspective

Ordered by how strongly the researchers Shan Li 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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 Facial Expression Recognition: A Survey (2018) — 1,694 citations
• Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild (2017) — 1,348 citations
• Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition (2019) — 601 citations