Yu Kong

Computer Vision and Pattern Recognition · 2014–2018

A researcher in the Science corpus: 6 papers; trusts 1,661 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 356 citations into it; #3,992 of 21,575 by trust here, #3,786 by citations
• Artificial Intelligence — 160 citations into it; #11,288 of 35,345 by trust here, #15,866 by citations
• Media Technology — 72 citations into it; #734 of 3,621 by trust here, #330 by citations
• Biomedical Engineering — 41 citations into it; #1,097 of 10,734 by trust here, #4,781 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Human-Computer Interaction — 23 citations into it
• Model Architectures — 22 citations into it

Whom Yu Kong trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ivan Laptev
7. Ross B. Girshick
8. Yann LeCun
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Yu Kong overall

1. Qiuhong Ke
2. Jun Liu
3. Amir Shahroudy
4. Jianfang Hu
5. Zehua Sun
6. Yun Raymond Fu
7. Xionghui Wang
8. G. Wang
9. Pichao Wang
10. Zhen Xu

Papers from Yu Kong's perspective

Ordered by how strongly the researchers Yu Kong 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Residual Dense Network for Image Super-Resolution (2018) — 3,919 citations
• Residual Dense Network for Image Restoration (2018) — 853 citations
• A Discriminative Model with Multiple Temporal Scales for Action Prediction (2014) — 164 citations
• Bilinear heterogeneous information machine for RGB-D action recognition (2015) — 82 citations
• Human Action Recognition and Prediction: A Survey (2018) — 806 citations
• Deep Sequential Context Networks for Action Prediction (2017) — 173 citations