Xian-Feng Han

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 588 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 99 citations into it; #13,814 of 21,575 by trust here, #15,955 by citations
• Computational Mechanics — 72 citations into it; #6,102 of 10,001 by trust here, #6,041 by citations
• 3D Vision & Pose — 44 citations into it
• Computer Graphics and Computer-Aided Design — 42 citations into it
• Aerospace Engineering — 27 citations into it
• Geology — 17 citations into it
• Generative Models — 15 citations into it
• Model Architectures — 6 citations into it

Whom Xian-Feng Han trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Diederik P. Kingma
4. Karen Simonyan
5. Alex Krizhevsky
6. Andrew Zisserman
7. Ross B. Girshick
8. Yoshua Bengio
9. Yann LeCun
10. Jiajun Wu

Who trusts Xian-Feng Han overall

1. Laurent Valentin Jospin
2. Hamid Laga
3. Yulan Guo
4. Farid Boussaïd
5. Mohammed Bennamoun
6. Wray L. Buntine
7. Hanyun Wang
8. Nantheera Anantrasirichai
9. Anh-Quan Cao

Papers from Xian-Feng Han's perspective

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

Their papers

• Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era (2019) — 417 citations
• A review of algorithms for filtering the 3D point cloud (2017) — 413 citations