Mengyuan Liu

Computer Vision and Pattern Recognition · with Liu · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 933 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 156 citations into it; #4,507 of 21,575 by trust here, #8,612 by citations
• Artificial Intelligence — 62 citations into it; #24,376 of 35,345 by trust here, #25,809 by citations
• Biomedical Engineering — 61 citations into it; #719 of 10,734 by trust here, #5,112 by citations
• Human-Computer Interaction — 58 citations into it; #213 of 2,253 by trust here, #604 by citations
• 3D Vision & Pose — 22 citations into it
• Model Architectures — 15 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Mengyuan Liu trusts overall

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

1. Karen Simonyan
2. Alex Krizhevsky
3. Lu Xia
4. Yong Du
5. Geoffrey E. Hinton
6. Andrea Vedaldi
7. Andrew Zisserman
8. Kaiming He
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Mengyuan Liu overall

1. Yifan Zhang
2. Jun Liu
3. Lei Shi
4. Pengfei Zhang
5. Chenyang Si
6. Jian Cheng
7. Amir Shahroudy
8. Pichao Wang
9. Cuiling Lan
10. Hanqing Lu

Papers from Mengyuan Liu's perspective

Ordered by how strongly the researchers Mengyuan Liu 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. Long Short-Term Memory
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Gradient-based learning applied to document recognition
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Enhanced skeleton visualization for view invariant human action recognition (2017) — 843 citations
• Exploiting Temporal Contexts With Strided Transformer for 3D Human Pose Estimation (2021) — 302 citations
• 3D action recognition using data visualization and convolutional neural networks (2017) — 17 citations
• Fusing shape and motion matrices for view invariant action recognition using 3D skeletons (2017) — 19 citations
• Recognizing Human Actions as the Evolution of Pose Estimation Maps (2018) — 322 citations