Jingwen Hu

Computer Vision and Pattern Recognition · with Hu · 2015–2016

A researcher in the Science corpus: 4 papers; trusts 684 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 177 citations into it; #6,213 of 21,575 by trust here, #4,525 by citations
• Media Technology — 72 citations into it; #689 of 3,621 by trust here, #289 by citations
• Artificial Intelligence — 35 citations into it; #21,736 of 35,345 by trust here, #10,667 by citations
• Representation & Self-Supervised Learning — 32 citations into it
• Remote Sensing — 24 citations into it
• Atmospheric Science — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it; #2,819 of 3,147 by trust here, #1,603 by citations
• Model Architectures — 9 citations into it

Whom Jingwen Hu trusts overall

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

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

Who trusts Jingwen Hu overall

1. Gong Cheng
2. Nassim Ammour
3. Gui-Song Xia
4. Xiaoqiang Lu
5. Junwei Han
6. Wei Zhang
7. Qiangqiang Yuan
8. Haikel Salem Alhichri
9. Yakoub Bazi
10. Souleyman Chaib

Papers from Jingwen Hu's perspective

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

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