Andrew Zhai

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 399 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 84 citations into it
• Artificial Intelligence — 51 citations into it
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 16 citations into it; #1,889 of 3,145 by trust here, #2,805 by citations
• Evaluation & Benchmarks — 14 citations into it; #1,669 of 3,147 by trust here, #2,853 by citations
• Optimization & Training — 10 citations into it
• Radiology, Nuclear Medicine and Imaging — 6 citations into it
• Signal Processing — 6 citations into it

Whom Andrew Zhai trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Christian Szegedy
4. Alex Krizhevsky
5. Ross B. Girshick
6. Jia Deng
7. Sergey Ioffe
8. Yann LeCun
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Andrew Zhai overall

1. Eu Wern Teh
2. Max Bain
3. Chao Jia
4. Jasmine Collins
5. Terrance Devries
6. Arsha Nagrani
7. David C. Liu
8. Zhe Zhao
9. Graham W. Taylor
10. Yinfei Yang

Papers from Andrew Zhai's perspective

Ordered by how strongly the researchers Andrew Zhai 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Toward Transformer-Based Object Detection (2020) — 257 citations
• Visual Discovery at Pinterest (2017) — 84 citations
• Classification is a Strong Baseline for Deep Metric Learning (2018) — 234 citations