P. Li

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 1,141 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 203 citations into it; #9,243 of 21,575 by trust here, #4,502 by citations
• Artificial Intelligence — 113 citations into it; #32,511 of 35,345 by trust here, #7,471 by citations
• Model Architectures — 54 citations into it
• Media Technology — 31 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Optimization & Training — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Computational Mechanics — 13 citations into it; #8,821 of 10,001 by trust here, #6,877 by citations

Whom P. Li trusts overall

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

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

Who trusts P. Li overall

1. Heliang Zheng
2. Wenbin Li
3. Jianlong Fu
4. Qilong Wang
5. Yunpeng Chen
6. Lei Huang
7. Jinglin Xu
8. Kiat Chuan Tan
9. Hao Wang
10. Xiu-Shen Wei

Papers from P. Li's perspective

Ordered by how strongly the researchers P. 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation (2017) — 650 citations
• ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (2019) — 6,251 citations
• Multi-scale Location-Aware Kernel Representation for Object Detection (2018) — 71 citations
• Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization (2017) — 305 citations
• Is Second-Order Information Helpful for Large-Scale Visual Recognition? (2017) — 280 citations
• End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks (2016) — 149 citations
• Global Second-Order Pooling Convolutional Networks (2018) — 413 citations