Fan Hu

Computer Vision and Pattern Recognition · 2013–2016

A researcher in the Science corpus: 6 papers; trusts 764 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 209 citations into it; #4,702 of 21,575 by trust here, #3,613 by citations
• Media Technology — 76 citations into it; #408 of 3,621 by trust here, #206 by citations
• Artificial Intelligence — 50 citations into it; #21,737 of 35,345 by trust here, #10,667 by citations
• Representation & Self-Supervised Learning — 41 citations into it; #2,027 of 3,145 by trust here, #1,943 by citations
• Remote Sensing — 21 citations into it; #23 of 95 by trust here, #19 by citations
• Ocean Engineering — 11 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Aerospace Engineering — 10 citations into it

Whom Fan Hu trusts overall

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

1. Geoffrey E. Hinton
2. Andrew Zisserman
3. Alex Krizhevsky
4. David G. Lowe
5. Yoshua Bengio
6. Yi Yang
7. Yann LeCun
8. Karen Simonyan
9. Kaiming He
10. Cordelia Schmid

Who trusts Fan Hu overall

1. Gong Cheng
2. Gui-Song Xia
3. Liangpei Zhang
4. Jingwen Hu
5. Junwei Han
6. Chaopeng Shen
7. Yanfei Zhong
8. Ying Li
9. Jian Kang
10. Xiaoqiang Lu

Papers from Fan Hu's perspective

Ordered by how strongly the researchers Fan Hu 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016) — 2,251 citations
• Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery (2015) — 1,145 citations
• Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery (2016) — 26 citations
• A Comparative Study of Sampling Analysis in the Scene Classification of Optical High-Spatial Resolution Remote Sensing Imagery (2015) — 35 citations
• Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification (2015) — 163 citations
• Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field (2013) — 25 citations