Ke Sun

Computer Vision and Pattern Recognition · IEEE Transactions on Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 1,051 researchers and is trusted by 96 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 404 citations into it; #3,312 of 21,575 by trust here, #2,111 by citations
• Artificial Intelligence — 241 citations into it; #10,245 of 35,345 by trust here, #3,229 by citations
• Graph Learning — 60 citations into it
• Human-Computer Interaction — 38 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Statistical and Nonlinear Physics — 32 citations into it
• Model Architectures — 31 citations into it
• Generative Models — 20 citations into it

Whom Ke Sun trusts overall

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

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

Who trusts Ke Sun overall

1. Yuhui Yuan
2. Jingdong Wang
3. Thorsten Hoeser
4. Simon Vandenhende
5. Bowen Cheng
6. Yinpeng Chen
7. Kaiwen Duan
8. Yuanduo Hong
9. Ce Zheng
10. Xiaokang Chen

Papers from Ke Sun's perspective

Ordered by how strongly the researchers Ke Sun 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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Deep High-Resolution Representation Learning for Human Pose Estimation (2019) — 5,208 citations
• Deep High-Resolution Representation Learning for Visual Recognition (2019) — 4,876 citations
• High-Resolution Representations for Labeling Pixels and Regions (2019) — 909 citations
• Graph Learning: A Survey (2021) — 510 citations
• Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression (2021) — 356 citations
• Multi-Stage Self-Supervised Learning for Graph Convolutional Networks (2019) — 310 citations
• Deep Feature Consistent Variational Autoencoder (2016) — 354 citations