Mingyi He

Computer Vision and Pattern Recognition · 2012–2020

A researcher in the Science corpus: 6 papers; trusts 1,083 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 213 citations into it; #7,534 of 21,575 by trust here, #5,890 by citations
• Artificial Intelligence — 55 citations into it
• Human-Computer Interaction — 44 citations into it; #1,406 of 2,253 by trust here, #1,588 by citations
• Biomedical Engineering — 41 citations into it; #6,130 of 10,734 by trust here, #8,509 by citations
• 3D Vision & Pose — 28 citations into it
• Computational Mechanics — 23 citations into it; #5,927 of 10,001 by trust here, #6,582 by citations
• Model Architectures — 19 citations into it
• Media Technology — 17 citations into it; #1,792 of 3,621 by trust here, #2,954 by citations

Whom Mingyi He trusts overall

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

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

Who trusts Mingyi He overall

1. Swalpa Kumar Roy
2. Xin He
3. Nicolas Audebert
4. Muhammad Ahmad
5. Bertrand Le Saux
6. Yushi Chen
7. Suvojit Manna
8. Gopal Krishna
9. Ravi Garg
10. Yucheng Chen

Papers from Mingyi He's perspective

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

Their papers

• Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs (2015) — 642 citations
• A Simple Prior-Free Method for Non-rigid Structure-from-Motion Factorization (2012) — 369 citations
• Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN (2017) — 259 citations
• Monocular human pose estimation: A survey of deep learning-based methods (2020) — 412 citations
• Multi-scale 3D deep convolutional neural network for hyperspectral image classification (2017) — 410 citations
• Skeleton boxes: Solving skeleton based action detection with a single deep convolutional neural network (2017) — 36 citations