Chang-Fu Tang

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 8 papers; trusts 801 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 320 citations into it; #4,754 of 21,575 by trust here, #4,834 by citations
• Artificial Intelligence — 123 citations into it; #12,483 of 35,345 by trust here, #10,667 by citations
• Biomedical Engineering — 107 citations into it; #1,703 of 10,734 by trust here, #3,650 by citations
• Human-Computer Interaction — 99 citations into it; #525 of 2,253 by trust here, #381 by citations
• 3D Vision & Pose — 31 citations into it; #534 of 689 by trust here, #504 by citations
• Model Architectures — 24 citations into it
• Control and Systems Engineering — 12 citations into it
• Endocrinology, Diabetes and Metabolism — 12 citations into it; #117 of 623 by trust here, #391 by citations

Whom Chang-Fu Tang trusts overall

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

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

Who trusts Chang-Fu Tang overall

1. Pichao Wang
2. Wanqing Li
3. Jun Liu
4. Yonghong Hou
5. Mengyuan Liu
6. Zhimin Gao
7. Philip O. Ogunbona
8. Chaolong Li
9. Zehua Sun
10. Jacopo Cavazza

Papers from Chang-Fu Tang's perspective

Ordered by how strongly the researchers Chang-Fu Tang 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

• Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices (2015) — 99 citations
• Action Recognition From Depth Maps Using Deep Convolutional Neural Networks (2016) — 293 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
• Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018) — 157 citations
• Late Fusion Incomplete Multi-view Clustering (2018) — 346 citations
• Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017) — 136 citations
• RGB-D-based action recognition datasets: A survey (2016) — 264 citations