Kai Li

Computer Vision and Pattern Recognition · with Fu · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 356 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 80 citations into it; #5,032 of 21,575 by trust here, #4,212 by citations
• Media Technology — 49 citations into it; #511 of 3,621 by trust here, #259 by citations
• Artificial Intelligence — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 5 citations into it

Whom Kai Li trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kai Zhang
5. Chao Dong
6. Alex Krizhevsky
7. Yoshua Bengio
8. Ross B. Girshick
9. Yann LeCun
10. Karen Simonyan

Who trusts Kai Li overall

1. Kai Zhang
2. Syed Waqas Zamir
3. Jingyun Liang
4. Jinjin Gu
5. Radu Timofte
6. Yulun Zhang
7. Saeed Anwar
8. Jie Liu
9. Aditya Arora
10. Salman Hameed Khan

Papers from Kai Li's perspective

Ordered by how strongly the researchers Kai Li 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. Gradient-based learning applied to document recognition
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Long Short-Term Memory

Their papers

• Image Super-Resolution Using Very Deep Residual Channel Attention Networks (2018) — 5,405 citations
• Residual Non-local Attention Networks for Image Restoration (2019) — 777 citations