Wenbin Chen

Computer Vision and Pattern Recognition · with Guo · 2013–2015

A researcher in the Science corpus: 4 papers; trusts 362 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 124 citations into it; #4,450 of 21,575 by trust here, #7,421 by citations
• Human-Computer Interaction — 44 citations into it; #161 of 2,253 by trust here, #369 by citations
• Artificial Intelligence — 42 citations into it; #15,386 of 35,345 by trust here, #21,878 by citations
• Biomedical Engineering — 38 citations into it; #629 of 10,734 by trust here, #4,314 by citations
• 3D Vision & Pose — 27 citations into it
• Endocrinology, Diabetes and Metabolism — 8 citations into it; #252 of 623 by trust here, #603 by citations
• Representation & Self-Supervised Learning — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Wenbin Chen trusts overall

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

1. Xiaodong Yang
2. Lu Xia
3. Zicheng Liu
4. Ivan Laptev
5. Christopher Evans
6. Trevor J. Hastie
7. Omar Oreifej
8. Yingli Tian
9. Wanqing Li
10. Geoffrey E. Hinton

Who trusts Wenbin Chen overall

1. Mengyuan Liu
2. Jun Liu
3. Zehua Sun
4. Jianfang Hu
5. Y. Zhu
6. Hong Liu
7. Guillermo Garcia-Hernando
8. Qiuhong Ke
9. Guo-qing Guo
10. Amir Shahroudy

Papers from Wenbin Chen's perspective

Ordered by how strongly the researchers Wenbin Chen 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. 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

• Evaluating spatiotemporal interest point features for depth-based action recognition (2014) — 121 citations
• Fusing Spatiotemporal Features and Joints for 3D Action Recognition (2013) — 238 citations
• TriViews: A general framework to use 3D depth data effectively for action recognition (2015) — 26 citations
• Fusing Multiple Features for Depth-Based Action Recognition (2015) — 45 citations