Zhimin Gao

Computer Vision and Pattern Recognition · with Wang · 2014–2018

A researcher in the Science corpus: 9 papers; trusts 891 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 323 citations into it; #5,099 of 21,575 by trust here, #5,464 by citations
• Artificial Intelligence — 136 citations into it; #20,392 of 35,345 by trust here, #16,771 by citations
• Biomedical Engineering — 108 citations into it; #1,129 of 10,734 by trust here, #2,835 by citations
• Human-Computer Interaction — 107 citations into it; #685 of 2,253 by trust here, #567 by citations
• 3D Vision & Pose — 35 citations into it; #536 of 689 by trust here, #504 by citations
• Model Architectures — 31 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Zhimin Gao trusts overall

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

1. Alex Krizhevsky
2. Xiaodong Yang
3. Pichao Wang
4. Wanqing Li
5. Geoffrey E. Hinton
6. Karen Simonyan
7. Ilya Sutskever
8. Andrew Zisserman
9. Kaiming He
10. Yoshua Bengio

Who trusts Zhimin Gao overall

1. Pichao Wang
2. Wanqing Li
3. Yonghong Hou
4. Jun Liu
5. Philip O. Ogunbona
6. Zehua Sun
7. Chuankun Li
8. Mengyuan Liu
9. Amir Shahroudy
10. Shuang Wang

Papers from Zhimin Gao's perspective

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

Their papers

• Action Recognition From Depth Maps Using Deep Convolutional Neural Networks (2016) — 293 citations
• Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation (2014) — 45 citations
• ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring (2015) — 103 citations
• Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (2016) — 98 citations
• Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks (2016) — 54 citations
• Spatially and Temporally Structured Global to Local Aggregation of Dynamic Depth Information for Action Recognition (2018) — 18 citations
• Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018) — 157 citations
• Structured Images for RGB-D Action Recognition (2017) — 57 citations
• Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017) — 136 citations