Qiangqiang Yuan

Computer Vision and Pattern Recognition · 2012–2024

A researcher in the Science corpus: 11 papers; trusts 411 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 181 citations into it; #6,702 of 21,575 by trust here, #7,499 by citations
• Media Technology — 137 citations into it; #751 of 3,621 by trust here, #521 by citations
• Computational Mechanics — 37 citations into it
• Artificial Intelligence — 35 citations into it
• Ecology — 30 citations into it
• Atmospheric Science — 26 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 16 citations into it

Whom Qiangqiang Yuan trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yann LeCun
4. Zhou Wang
5. Alex Krizhevsky
6. Yoshua Bengio
7. Diederik P. Kingma
8. Ross B. Girshick
9. Boris Polyak
10. Christian Szegedy

Who trusts Qiangqiang Yuan overall

1. José M. Bioucas-Dias
2. Wei He
3. Hongyan Zhang
4. Huanfeng Shen
5. Lina Zhuang
6. Liangpei Zhang
7. Xiaoqiang Lu
8. Behnood Rasti
9. Jiaxin Li
10. Xian Guo

Papers from Qiangqiang Yuan's perspective

Ordered by how strongly the researchers Qiangqiang Yuan 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution (2024) — 339 citations
• Deep learning in environmental remote sensing: Achievements and challenges (2020) — 1,475 citations
• Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network (2018) — 364 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
• Hyperspectral Image Restoration Using Low-Rank Matrix Recovery (2014) — 821 citations
• Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model (2012) — 587 citations