Enhua Wu

Computer Vision and Pattern Recognition · 2013–2022

A researcher in the Science corpus: 5 papers; trusts 862 researchers and is trusted by 1,957 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 232 citations into it; #905 of 21,575 by trust here, #676 by citations
• Artificial Intelligence — 160 citations into it; #1,515 of 35,345 by trust here, #968 by citations
• Model Architectures — 98 citations into it; #169 of 2,278 by trust here, #101 by citations
• Optimization & Training — 38 citations into it
• Efficiency & Compression — 21 citations into it
• Graph Learning — 19 citations into it
• Neural Architecture Search & AutoML — 19 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Enhua Wu trusts overall

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

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

Who trusts Enhua Wu overall

1. Kai Han
2. Wenhai Wang
3. Xiangxiang Chu
4. Liang-Chieh Chen
5. Jianyuan Guo
6. Hugo Touvron
7. Salman Hameed Khan
8. Jianwei Yang
9. Weihao Yu
10. Ningning Ma

Papers from Enhua Wu's perspective

Ordered by how strongly the researchers Enhua Wu 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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
• Transformer in Transformer (2021) — 2,223 citations
• Squeeze-and-Excitation Networks (2017) — 1,495 citations
• Constant Time Weighted Median Filtering for Stereo Matching and Beyond (2013) — 303 citations
• Vision GNN: An Image is Worth Graph of Nodes (2022) — 633 citations