Yonghong Hou

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 6 papers; trusts 582 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 267 citations into it; #5,063 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 124 citations into it
• Biomedical Engineering — 104 citations into it; #784 of 10,734 by trust here, #3,764 by citations
• Human-Computer Interaction — 87 citations into it; #223 of 2,253 by trust here, #416 by citations
• 3D Vision & Pose — 45 citations into it
• Model Architectures — 23 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Endocrinology, Diabetes and Metabolism — 13 citations into it

Whom Yonghong Hou trusts overall

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

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

Who trusts Yonghong Hou overall

1. Pichao Wang
2. Wanqing Li
3. Chuankun Li
4. Zhimin Gao
5. Mengyuan Liu
6. Philip O. Ogunbona
7. Bo Li
8. Jun Liu
9. Zehua Sun
10. Song Liu

Papers from Yonghong Hou's perspective

Ordered by how strongly the researchers Yonghong Hou 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. Gradient-based learning applied to document recognition
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

• Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks (2016) — 386 citations
• Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks (2018) — 326 citations
• Joint Distance Maps Based Action Recognition With Convolutional Neural Networks (2017) — 302 citations
• Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks (2016) — 234 citations
• Spatially and Temporally Structured Global to Local Aggregation of Dynamic Depth Information for Action Recognition (2018) — 18 citations
• Structured Images for RGB-D Action Recognition (2017) — 57 citations