Shih-En Wei

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 882 researchers and is trusted by 2,410 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 160 citations into it; #788 of 21,575 by trust here, #1,364 by citations
• Artificial Intelligence — 30 citations into it; #1,572 of 35,345 by trust here, #1,904 by citations
• Human-Computer Interaction — 30 citations into it
• Biomedical Engineering — 24 citations into it
• Model Architectures — 24 citations into it
• 3D Vision & Pose — 13 citations into it; #93 of 689 by trust here, #69 by citations
• Optimization & Training — 8 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Shih-En Wei trusts overall

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

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

Who trusts Shih-En Wei overall

1. Kaiming He
2. Juergen Gall
3. Xuanyi Dong
4. Georgios Pavlakos
5. Xingyi Zhou
6. Hanbyul Joo
7. Wanli Ouyang
8. Georgia Gkioxari
9. Tomas Simon
10. Grégory Rogez

Papers from Shih-En Wei's perspective

Ordered by how strongly the researchers Shih-En Wei 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Convolutional Pose Machines (2016) — 2,922 citations
• Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields (2016) — 7,345 citations
• OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields (2018) — 5,711 citations
• Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors (2018) — 206 citations