Gang Sun

Computer Vision and Pattern Recognition · with Hu · 2015–2022

A researcher in the Science corpus: 6 papers; trusts 921 researchers and is trusted by 2,083 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 268 citations into it; #875 of 21,575 by trust here, #651 by citations
• Artificial Intelligence — 178 citations into it; #1,289 of 35,345 by trust here, #876 by citations
• Model Architectures — 111 citations into it; #204 of 2,278 by trust here, #119 by citations
• Optimization & Training — 34 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Neural Architecture Search & AutoML — 19 citations into it
• Efficiency & Compression — 18 citations into it
• Media Technology — 14 citations into it

Whom Gang Sun trusts overall

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

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

Who trusts Gang Sun overall

1. Christoph Feichtenhofer
2. Meng-Hao Guo
3. Liang-Chieh Chen
4. Quoc V. Le
5. Irwan Bello
6. Qibin Hou
7. Li Yuan
8. Kai Han
9. Barret Zoph
10. Yunpeng Chen

Papers from Gang Sun's perspective

Ordered by how strongly the researchers Gang Sun 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. 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

• Squeeze-and-Excitation Networks (2017) — 35,229 citations
• Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks (2018) — 700 citations
• Deep Image: Scaling up Image Recognition (2015) — 385 citations
• Squeeze-and-Excitation Networks (2017) — 1,495 citations
• A Key Volume Mining Deep Framework for Action Recognition (2016) — 240 citations
• HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification (2022) — 352 citations