Peng Tang

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 726 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 119 citations into it; #7,254 of 21,575 by trust here, #6,795 by citations
• Artificial Intelligence — 53 citations into it; #12,258 of 35,345 by trust here, #11,327 by citations
• Model Architectures — 15 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Optimization & Training — 9 citations into it
• Aerospace Engineering — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Media Technology — 5 citations into it

Whom Peng Tang trusts overall

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

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

Who trusts Peng Tang overall

1. Fang Wan
2. Zeyi Huang
3. Dingwen Zhang
4. Elad Amrani
5. Gao Yan
6. Aditya Arun
7. Weifeng Ge
8. Yongqiang Zhang
9. Naoto Inoue
10. Jiajie Wang

Papers from Peng Tang's perspective

Ordered by how strongly the researchers Peng Tang 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. 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

• PCL: Proposal Cluster Learning for Weakly Supervised Object Detection (2018) — 405 citations
• Multiple Instance Detection Network with Online Instance Classifier Refinement (2017) — 481 citations
• Revisiting multiple instance neural networks (2016) — 517 citations
• Shape-Texture Debiased Neural Network Training (2020) — 127 citations