Shuaifeng Zhi

Computer Vision and Pattern Recognition · 2021

A researcher in the Science corpus: 2 papers; trusts 267 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 52 citations into it; #20,636 of 21,575 by trust here, #13,542 by citations
• Artificial Intelligence — 28 citations into it
• Aerospace Engineering — 12 citations into it; #6,022 of 6,421 by trust here, #3,519 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Computer Graphics and Computer-Aided Design — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Computational Mechanics — 5 citations into it

Whom Shuaifeng Zhi trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ross B. Girshick
6. John McCormac
7. Yoshua Bengio
8. Rob Fergus
9. Yann LeCun
10. Liang-Chieh Chen

Who trusts Shuaifeng Zhi overall

1. Vincent Sitzmann
2. Sosuke Kobayashi
3. Chandan Yeshwanth
4. Minghan Qin
5. Justin Kerr
6. Konstantinos Rematas
7. Abhijit Kundu
8. Eiichi Matsumoto
9. Yueh-Cheng Liu
10. Semon Rezchikov

Papers from Shuaifeng Zhi's perspective

Ordered by how strongly the researchers Shuaifeng Zhi 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• In-Place Scene Labelling and Understanding with Implicit Scene Representation (2021) — 560 citations
• Bootstrapping Semantic Segmentation with Regional Contrast (2021) — 159 citations