Huanfeng Shen

Computer Vision and Pattern Recognition · 2010–2020

A researcher in the Science corpus: 15 papers; trusts 392 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 206 citations into it; #6,847 of 21,575 by trust here, #7,176 by citations
• Media Technology — 165 citations into it; #628 of 3,621 by trust here, #417 by citations
• Computational Mechanics — 52 citations into it; #6,679 of 10,001 by trust here, #4,937 by citations
• Artificial Intelligence — 51 citations into it
• Ecology — 49 citations into it; #6,887 of 10,717 by trust here, #5,577 by citations
• Atmospheric Science — 47 citations into it
• Environmental Engineering — 38 citations into it
• Biomedical Engineering — 15 citations into it

Whom Huanfeng Shen trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Zhou Wang
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Per Jönsson
8. Stanley J. Osher
9. Michael Elad
10. Diederik P. Kingma

Who trusts Huanfeng Shen overall

1. Qiangqiang Yuan
2. Tongwen Li
3. Qiang Zhang
4. Liangpei Zhang
5. Lina Zhuang
6. José M. Bioucas-Dias
7. Behnood Rasti
8. Wei He
9. Billy Pik Lik Lau
10. Alexander Y. Sun

Papers from Huanfeng Shen's perspective

Ordered by how strongly the researchers Huanfeng Shen 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep learning in environmental remote sensing: Achievements and challenges (2020) — 1,475 citations
• Land-cover classification with high-resolution remote sensing images using transferable deep models (2018) — 979 citations
• Hyperspectral Image Denoising Employing a Spatial–Spectral Deep Residual Convolutional Neural Network (2018) — 449 citations
• Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach (2017) — 376 citations
• A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening (2017) — 580 citations
• Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network (2017) — 409 citations
• Image super-resolution: The techniques, applications, and future (2016) — 537 citations
• Point-surface fusion of station measurements and satellite observations for mapping PM 2.5 distribution in China: Methods and assessment (2016) — 201 citations
• Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China (2016)
• Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration (2016) — 631 citations
• Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015) — 517 citations
• Hyperspectral Image Restoration Using Low-Rank Matrix Recovery (2014) — 821 citations
• Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method (2013) — 296 citations
• Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model (2012) — 587 citations
• A super-resolution reconstruction algorithm for surveillance images (2010) — 367 citations