Z. Q. Lin

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 204 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 51 citations into it; #8,114 of 21,575 by trust here, #7,657 by citations
• Artificial Intelligence — 39 citations into it
• Radiology, Nuclear Medicine and Imaging — 29 citations into it; #610 of 3,265 by trust here, #1,081 by citations
• Model Architectures — 13 citations into it
• Optimization & Training — 5 citations into it

Whom Z. Q. Lin trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. X. Zhang
6. Shaoqing Ren
7. Karen Simonyan
8. Yann LeCun
9. Andrew Zisserman
10. Yoshua Bengio

Who trusts Z. Q. Lin overall

1. Ferhat Ucar
2. Muhammad Shoaib Farooq
3. Asmaa Abbas
4. Asif Iqbal Khan
5. Aishwarya Kumar
6. Yujin Oh
7. Joseph Aylett-Bullock
8. Qiang Yang
9. Yuhao Zhang
10. Md. Zahangir Alom

Papers from Z. Q. Lin's perspective

Ordered by how strongly the researchers Z. Q. Lin 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Squeeze-and-Attention Networks for Semantic Segmentation (2019) — 287 citations
• COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020) — 2,747 citations