Shuang Liang

Computer Vision and Pattern Recognition · with Wei · 2014–2017

A researcher in the Science corpus: 6 papers; trusts 885 researchers and is trusted by 222 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 205 citations into it; #2,529 of 21,575 by trust here, #3,313 by citations
• Human-Computer Interaction — 61 citations into it; #93 of 2,253 by trust here, #125 by citations
• Artificial Intelligence — 41 citations into it
• Biomedical Engineering — 22 citations into it
• Control and Systems Engineering — 22 citations into it; #3,015 of 8,257 by trust here, #1,252 by citations
• 3D Vision & Pose — 19 citations into it; #196 of 689 by trust here, #249 by citations
• Evaluation & Benchmarks — 15 citations into it
• Aerospace Engineering — 14 citations into it

Whom Shuang Liang trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Christian Szegedy
7. Jia Deng
8. Yoshua Bengio
9. Diederik P. Kingma
10. Andrew Zisserman

Who trusts Shuang Liang overall

1. Gyeongsik Moon
2. Jiefeng Li
3. Huchuan Lu
4. Lijun Wang
5. Long Zhao
6. Xiao Sun
7. Georgios Pavlakos
8. Xingyi Zhou
9. Jianming Zhang
10. Kyoung Mu Lee

Papers from Shuang Liang's perspective

Ordered by how strongly the researchers Shuang Liang 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Compositional Human Pose Regression (2017) — 590 citations
• Deep Kinematic Pose Regression (2016) — 277 citations
• Integral Human Pose Regression (2017) — 942 citations
• Cascaded hand pose regression (2015) — 411 citations
• Saliency Optimization from Robust Background Detection (2014) — 1,313 citations
• Object proposal by multi-branch hierarchical segmentation (2015) — 40 citations